Systems and methods for managing voltage event alarms in an electrical system

ABSTRACT

Systems and methods for managing voltage event alarms in an electrical system are provided. In one aspect of this disclosure, a method for managing voltage event alarms in an electrical system includes processing electrical measurement data from, or derived from, energy-related signals captured by at least one intelligent electronic device (IED) to identify an anomalous voltage condition at a point of installation in the electrical system. The anomalous voltage condition may correspond, for example, to a measured IED voltage being above or greater than one or more upper alarm thresholds or below or less than one or more lower alarm thresholds. The method also includes determining if the electrical system is affected by the identified anomalous voltage condition. In response to determining that the electrical system is affected by the identified anomalous voltage condition, at least one of a plurality of criteria may be chosen to adjust at least one of the upper alarm thresholds and/or at least one of the lower alarm thresholds.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a Continuation-in-Part (CIP) application of andclaims the benefit of and priority to U.S. application Ser. No.16/233,231, filed on Dec. 27, 2018, which claims the benefit of andpriority to U.S. Provisional Application No. 62/694,791, filed on Jul.6, 2018; U.S. Provisional Application No. 62/770,730, filed on Nov. 21,2018; U.S. Provisional Application No. 62/770,732, filed on Nov. 21,2018; U.S. Provisional Application No. 62/770,737, filed on Nov. 21,2018; and U.S. Provisional Application No. 62/770,741, filed on Nov. 21,2018, all of which applications are incorporated by reference herein intheir entirety.

FIELD

This disclosure relates generally to power quality issues, and moreparticularly, to systems and methods related to managing voltage eventalarms associated with power quality issues in an electrical system.

BACKGROUND

As is known, power quality issues are one of the most significant andcostly impacts on electrical systems (also sometimes referred to as“electrical networks”). Poor power quality is estimated to cost theEuropean economy up to €150 billion annually, according to the LeonardoPower Quality Initiative.¹ Additionally, the U.S. economy experienceslosses ranging from $119 billion to $188 billion annually, according toresearch by the Electric Power Research Institute (EPRI).² Perhaps themost important statistic is the EPRI finding that 80 percent ofpower-quality disturbances are generated within a facility. Oneexemplary economic model summarizes the total cost associated with powerquality events as follows:

Total losses=production losses+restart losses+product/materiallosses+equipment losses+third-party costs+other miscellaneous costs³

¹https://adfpowertuning.com/en/about-us/news-stories/148-leonardo-energy-qpan-european-power-quality-surveyq-shows-g150bn-annually-in-cost-for-low-power-quality.html²https://blog.schneider-electric.com/power-management-metering-monitoring-power-quality/2015/10/16/why-poor-power-quality-costs-billions-annually-and-what-can-be-done-about-it/³The Cost of Poor Power Quality, Roman Targosz and David Chapman, October2015, ECI Publication No. Cu0145

Other miscellaneous costs associated with power quality issues mayinclude intangible losses such as a damaged reputation with customersand suppliers or more direct losses such as the devaluation of creditratings and stock prices.

SUMMARY

Described herein are systems and methods related to managing voltageevent alarms associated with power quality issues or events in anelectrical system. The electrical system may be associated with at leastone load, process, building, facility, watercraft, aircraft, or othertype of structure, for example. The electrical system may be monitoredand/or controlled by one or more electrical/power monitoring systems,for example.

In one aspect of this disclosure, a method for managing voltage eventalarms in an electrical system includes processing electricalmeasurement data from, or derived from, energy-related signals capturedby at least one IED of a plurality of IEDs to identify an anomalousvoltage condition (e.g., a power quality issue or event) at a point ofinstallation of a respective one of the plurality of IEDs in theelectrical system. In embodiments, the anomalous voltage conditioncorresponds to a measured IED voltage being above or greater than one ormore upper voltage event alarm thresholds or below or less than one ormore lower voltage event alarm thresholds, for example, as shown inFIGS. 49 and 50. The method also includes determining if the electricalsystem is affected (or “impacted,” as sometimes referred to herein) bythe identified anomalous voltage condition. In embodiments, in responseto determining that the electrical system is not affected by theidentified anomalous voltage condition, at least one of the one or moreupper alarm thresholds or at least one of the one or more lower alarmthreshold is adjusted to at least a magnitude of the measured IEDvoltage captured during the identified anomalous voltage condition. Insome embodiments, the at least one of the one or more upper alarmthresholds or the at least one of the one or more lower alarm thresholdsis also adjusted to a duration of the measured IED voltage.

In some embodiments, the above-discussed method (and/or other systemsand/or methods discussed herein) may be implemented on the at least oneIED called for in the above-discussed method. Additionally, in someembodiments the above-discussed method (and/or other systems and/ormethods discussed herein) may be implemented partially or fully remotefrom the at least one IED, for example, on other IEDs of the pluralityof IEDs and/or a control system or device coupled to the at least oneIED and/or the other IEDs. The above-discussed method (and/or othersystems and/or methods discussed herein) may also be implementedpartially or fully in a gateway, a cloud-based system, on-site software,a remote server, etc. (which may alternatively be referred to as a“head-end” or “Edge” system herein). Examples of the plurality of IEDs(including the at least one IED) may include a smart utility meter, apower quality meter, and/or another measurement device (or devices). Theplurality of IEDs may include breakers, relays, power quality correctiondevices, uninterruptible power supplies (UPSs), filters, and/or variablespeed drives (VSDs), for example. Additionally, the plurality of IEDsmay include at least one virtual meter in some embodiments. Inembodiments, the plurality of IEDs may also incorporate analog and/ordigital I/O capabilities from equipment directly or indirectly connectedto the electrical system. For example, ambient temperature readings(e.g., ° F., ° C.) from outside a facility and connected to the at leastone IED would be considered as an analog input connected to theelectrical system. Additionally, a breaker status (e.g., off/on,open/closed) derived from a breaker located inside switchgear within thefacility and brought into the at least one IED would be considered as adigital input connected to the electrical system.

In embodiments in which the above-discussed method (and/or other systemsand/or methods discussed herein) is implemented on the at least one IED,for example, the at least one IED may be coupled to measureenergy-related signals, receive the electrical measurement data at aninput, and configured to generate at least one or more outputs. Theoutputs may be used, for example, to manage voltage event alarms andalarm thresholds associated with anomalous voltage conditions in theelectrical system.

It is understood that an anomalous voltage condition may refer to anytype of electrical occurrence of interest. What is considered ananomalous voltage condition for one installation, for example, may notbe considered as an anomalous voltage condition for anotherinstallation. Accordingly, a “power quality” event, for example, is anelectrical occurrence of interest that is generally recognized as ananomalous voltage condition that may adversely impact the operationand/or equipment installed on an electrical system.

The above-discussed method (and/or other systems and/or methodsdiscussed herein) may include one or more of the following featureseither individually or in combination with other features in someembodiments. For example, in some embodiments the energy-related signalscaptured by the at least one IED may include at least one of a voltagesignal, a current signal, input/output (I/O) data, and a derived orextracted value. In some embodiments, the I/O data includes at least oneof a digital signal (e.g., two discrete states) and an analog signal(e.g., continuously variable). The digital signal may include, forexample, at least one of on/off status(es), open/closed status(es),high/low status(es), synchronizing pulse and any other representativebi-stable signal. Additionally, the analog signal may include, forexample, at least one of temperature, pressure, volume, spatial, rate,humidity, and any other physically or user/usage representative signal.

In accordance with some embodiments of this disclosure, the derived orextracted value includes at least one of a calculated, computed,estimated, derived, developed, interpolated, extrapolated, evaluated,and otherwise determined additional energy-related value from at leastone of the measured voltage signal and/or the measured current signal.In some embodiments, the derived value additionally or alternativelyincludes at least one of active power(s), apparent power(s), reactivepower(s), energy(ies), harmonic distortion(s), power factor(s),magnitude/direction of harmonic power(s), harmonic voltage(s), harmoniccurrent(s), interharmonic current(s), interharmonic voltage(s),magnitude/direction of interharmonic power(s), magnitude/direction ofsub-harmonic power(s), individual phase current(s), phase angle(s),impedance(s), sequence component(s), total voltage harmonicdistortion(s), total current harmonic distortion(s), three-phasecurrent(s), phase voltage(s), line voltage(s), spectral analysis and/orother similar/related parameters. In some embodiments, the derived valueadditionally or alternatively includes at least one energy-relatedcharacteristic, the energy-related characteristic including magnitude,direction, phase angle, percentage, ratio, level, duration, associatedfrequency components, energy-related parameter shape, and/or decay rate.In accordance with some embodiments of this disclosure, the derived orextracted value may be linked to at least one process, load(s)identification, etc., for example.

It is understood that the energy-related signals captured by the atleast one IED may include (or leverage) substantially any electricalparameter derived from at least one of the voltage and current signals(including the voltages and currents themselves), for example. It isalso understood that the energy-related signals may be continuously orsemi-continuously/periodically captured/recorded and/or transmittedand/or logged by the at least one IED, and anomalous voltage conditionsdetected based on the energy-related signals, and associated alarmthresholds, may be updated (e.g., evaluated/re-evaluated,prioritized/re-prioritized, tracked, etc.) in response thereto. Forexample, anomalous voltage conditions may initially be detected andcharacterized (e.g., as particular types of power quality events) fromenergy-related signals captured at a first time, and may be updated orrevised (e.g., recharacterized) in response to variation(s)/change(s)identified from energy-related signals captured at a second time.Associated alarm thresholds (e.g., the recited upper alarm thresholdsand lower alarm thresholds) may also be updated or revised.

In some embodiments, the at least one IED capturing the energy-relatedsignals includes at least one metering device. The at least one meteringdevice may correspond, for example, to at least one metering device inthe electrical system for which the energy-related signals aremonitored.

As used herein, an IED is a computational electronic device optimized toperform a particular function or set of functions. Examples of IEDs mayinclude smart utility meters, power quality meters, microprocessorrelays, digital fault recorders, and other metering devices. IEDs mayalso be imbedded in VSDs, uninterruptible power supplies (UPSs), circuitbreakers, relays, transformers, or any other electrical apparatus. IEDsmay be used to perform measurement/monitoring and control functions in awide variety of installations. The installations may include utilitysystems, industrial facilities, warehouses, office buildings or othercommercial complexes, campus facilities, computing co-location centers,data centers, power distribution networks, or any other structure,process or load that uses electrical energy. For example, where the IEDis an electrical power monitoring device, it may be coupled to (or beinstalled in) an electrical power transmission or distribution systemand configured to sense/measure and store data (e.g., waveform data,logged data, I/O data, etc.) as electrical parameters representingoperating characteristics (e.g., voltage, current, waveform distortion,power, etc.) of the electrical distribution system. These parameters andcharacteristics may be analyzed by a user to evaluate potentialperformance, reliability and/or power quality-related issues, forexample. The IED may include at least a controller (which in certainIEDs can be configured to run one or more applications simultaneously,serially, or both), firmware, a memory, a communications interface, andconnectors that connect the IED to external systems, devices, and/orcomponents at any voltage level, configuration, and/or type (e.g., AC,DC). At least certain aspects of the monitoring and controlfunctionality of an IED may be embodied in a computer program that isaccessible by the IED.

In some embodiments, the term “IED” as used herein may refer to ahierarchy of IEDs operating in parallel and/or tandem/series. Forexample, an IED may correspond to a hierarchy of energy meters, powermeters, and/or other types of resource meters. The hierarchy maycomprise a tree-based hierarchy, such a binary tree, a tree having oneor more child nodes descending from each parent node or nodes, orcombinations thereof, wherein each node represents a specific IED. Insome instances, the hierarchy of IEDs may share data or hardwareresources and may execute shared software. It is understood thathierarchies may be non-spatial such as billing hierarchies where IEDsgrouped together may be physically unrelated.

It is understood that an input is data that a processor and/or IED(e.g., the above-discussed plurality of IEDs) receives, and an output isdata that a processor and/or IED sends. Inputs and outputs may either bedigital or analog. The digital and analog signals may be both discretevariables (e.g., two states such as high/low, one/zero, on/off, etc. Ifdigital this may be a value. If analog, the presence of avoltage/current may be considered by the system/IED as an equivalentsignal) or continuous variables (e.g., continuously variable such asspatial position, temperature, pressure voltage, etc.). They may bedigital signals (e.g., measurements in an IED coming from a sensorproducing digital information/values) and/or analog signals (e.g.,measurements in an IED coming from a sensor producing analoginformation/values). These digital and/or analog signals may include anyprocessing step within the IED (e.g., derive a Power Factor, amagnitude, among all the derived calculations).

Processors and/or IEDs may convert/reconvert digital and analog inputsignals to a digital representation for internal processing. Processorsand/or IEDs may also be used to convert/reconvert internally processeddigital signals to digital and/or analog output signals to provide someindication, action, or other response (such as an input for anotherprocessor/IED). Typical uses of digital outputs may include opening orclosing breakers or switches, starting or stopping motors and/or otherequipment, and operating other devices and equipment that are able todirectly interface with digital signals. Digital inputs are often usedto determine the operational status/position of equipment (e.g., is abreaker open or closed, etc.) or read an input synchronous signal from autility pulsed output. Analog outputs may be used to provide variablecontrol of valves, motors, heaters, or other loads/processes in energymanagement systems. Finally, analog inputs may be used to gathervariable operational data and/or in proportional control schemes.

A few more examples where digital and analog I/O data are leveraged mayinclude (but not be limited to): turbine controls, plating equipment,fermenting equipment, chemical processing equipment, telecommunications,equipment, precision scaling equipment, elevators and moving sidewalks,compression equipment, waste water treatment equipment, sorting andhandling equipment, plating equipment temperature/pressure data logging,electrical generation/transmission/distribution, robotics, alarmmonitoring and control equipment, as a few examples.

As noted earlier in this disclosure, the energy-related signals measuredby the plurality of IEDs may include I/O data. It is understood that theI/O data may take the form of digital I/O data, analog I/O data, or acombination digital and analog I/O data. The I/O data may convey statusinformation, for example, and many other types of information, as willbe apparent to one of ordinary skill in the art from discussions aboveand below.

It is understood that the terms “processor” and “controller” aresometimes used interchangeably herein. For example, a processor may beused to describe a controller. Additionally, a controller may be used todescribe a processor.

In some embodiments, the above-discussed method (and/or other systemsand/or methods discussed herein) is generally applicable to non-periodicpower quality issues or events such as transients, short-durationroot-mean-square (rms) variations (e.g., sags, swells, momentaryinterruptions, temporary interruptions, etc.), and some long-durationrms variations (e.g., that may be as long as about 1-5 minute(s)).

Examples of electrical measurement data processed from, or derived from,energy-related signals that may be captured by the at least one IED toidentify the anomalous voltage condition may include at least one ofcontinuously measured voltage and current signals and their derivedparameters and characteristics. Electrical parameters and events may bederived, for example, from analyzing energy-related signals (e.g., realpower, reactive power, apparent power, harmonic distortion, phaseimbalance, frequency, voltage/current transients, voltage sags, voltageswells, etc.). More particularly, the at least one IED may evaluate apower quality event's magnitude, duration, load impact, recovery timefrom impact, unproductive recovery energy consumed, CO₂ emissions fromrecovery energy, costs associated with the event, and so forth.

It is understood there are types of power quality events and there arecertain characteristics of these types of power quality events, asdescribed further below in connection with the table from IEEE Standard1159-2019 (known art) provided below, for example. A voltage sag is oneexample type of power quality event. The characteristics of voltage sagevents are the magnitude of the voltage sag and its duration, forexample. The above-discussed method (and/or other systems and/or methodsdiscussed herein) may evaluate and adjust voltage event alarms based onthe affect (or impact) of voltage sag events (and other events) on theelectrical system. In embodiments, voltage event alarm thresholds areadjusted based on the voltage sag's magnitude and duration. In someembodiments, an anomalous voltage condition (i.e., voltage perturbation)is indicative of a power quality event (e.g., a voltage sag). As usedherein, examples of power quality events may include voltage and/orcurrent events on phase conductors, neutral conductors, and/or groundpaths. As illustrated in FIG. 3, for example, as will be describedfurther below, is some embodiments there will be multiple alarmthresholds, for example, depending on the duration of an event.Additionally, there may be a threshold “above the nominal” (e.g.,transients, swells, overvoltages), and a threshold “below the nominal”(e.g., sags, interruptions, undervoltages).

In accordance with some embodiments of this disclosure, the at least oneIED responsible for capturing the energy-related signals (which are usedto identify the anomalous voltage condition) is configured to monitorone or more loads in the electrical system (e.g., at the point ofinstallation of the respective one of the plurality of IEDs monitored bythe at least one IED). In embodiments, each of the loads has a ratedvoltage and recommended operational range, for example, as shown inFIGS. 49 and 50. The rated voltage corresponds to a desired voltagemagnitude/level for optimal load operation. Additionally, therecommended operational range is the area above and below the ratedvoltage where the loads may still operate continuously, although notnecessarily optimally. In some embodiments, the upper and lower alarmthresholds may be configured inside (or within) the recommendedoperational range of the loads to ensure the loads retain theiroperational integrity and are not overly stressed, and to ensure theend-user knows if/when they are getting close to the recommendedboundaries. In other embodiments, the upper and the lower alarmthresholds align with the recommended operational range of the loads sothat excursions beyond the recommended operational range may bemeasured, captured and stored (e.g., on a memory device associated withthe IED or electrical system). Additional aspects of rated voltages andrecommended operational ranges of loads, and their relationship to theupper and lower alarm thresholds, are described further in connectionwith FIGS. 49 and 50, for example.

In some embodiments, determining if the electrical system is affected bythe identified anomalous voltage condition includes determining if oneor more loads monitored by the respective one of the plurality of IEDsare affected by the identified anomalous voltage condition based on ameasured magnitude and/or duration of excursions beyond the recommendedvoltage range for the one or more loads. The recommended operationalrange may be substantially equal to or within the bounds of the upperand lower alarm thresholds, for example. In embodiments, each of the oneor more upper alarm thresholds and each of the one or more lower alarmthresholds has an associated magnitude and duration. The one or moreupper alarm thresholds may include a threshold above a nominal orexpected voltage at the point of installation of the respective one ofthe plurality of IEDs in the electrical system. The threshold above thenominal voltage may be indicative of transients, swells, orovervoltages, for example. The one or more lower alarm thresholds mayinclude a threshold below a nominal or expected voltage at the point ofinstallation of the respective one of the plurality of IEDs in theelectrical system. The threshold below the nominal voltage may beindicative of sags, interruptions, or undervoltages, for example.

In embodiments, determining if the electrical system is affected by theidentified anomalous voltage condition includes identifying loss of loadin the electrical system associated with the identified anomalousvoltage condition, and generating and/or initiating a load loss alarmindicating the identified loss of load. In some embodiments, the loadloss alarm may be communicated via at least one of: a report, a text, anemail, audibly, and an interface of a screen/display. The screen/displaymay be a screen/display of user device, for example, of a userresponsible for monitoring the electrical system. Additionally, inembodiments the load loss alarm may be indicated or displayed on asystem for managing voltage event alarms in the electrical system. Inembodiments, the electrical system includes one or more loads installedat respective locations in the electrical system, and the loads aremonitored by one or more of the IEDs in the electrical system. Loss ofload may be associated with the monitored loads, for example. Inembodiments, the loss of load may also be determined using virtualmeters, status inputs, etc.

As used herein, a load loss (sometimes also referred to as a “loss ofload”) is the unexpected, unplanned and/or unintentional removal of oneor more loads from the electrical system. In this application, a voltageperturbation or event, and the subsequent load loss, is likely a resultof one or more external influences on the electrical system (e.g., afault, etc.), or the normal or abnormal operation of loads, protectivedevices, mitigation devices, and/or other equipment intentionallyconnected to the electrical system. Load losses may be indicated bymeasured parameters such as voltage, current, power, energy, harmonicdistortion, imbalance, etc., or they may be indicated by discrete(digital) and/or analog input-output (I/O) signals originating fromequipment directly and/or indirectly connected to the electrical system.For example, breakers often provide an output indication on theirpresent position (e.g., open/closed, off/on, etc.) to communicate theiroperational status.

In embodiments, determining if the electrical system is affected by theidentified anomalous voltage condition includes determining if one ormore portions or zones of the electrical system proximate to therespective one of the plurality of IEDs is affected by the identifiedanomalous voltage condition. In some embodiments, the at least one IEDof the plurality of IEDs includes the respective one of the plurality ofIEDs. Additionally, in some embodiments the respective one of theplurality of IEDs is installed at a first point of installation in theelectrical system, and at least one IED of the plurality of IEDs isinstalled in at least the first point of installation in the electricalsystem and a second point of installation in the electrical system. Inembodiments, the first and second points of installation in theelectrical system correspond to first and second hierarchical zones ofthe electrical system.

Additionally, in embodiments determining if the electrical system isaffected by the identified anomalous voltage condition includesmeasuring one or more first parameters associated with the electricalsystem at a first time, measuring one or more second parametersassociated with the electrical system at a second time, and comparingthe first parameters to the second parameters to determine if theelectrical system is affected by the identified anomalous voltagecondition. In embodiments, comparing the first parameters to the secondparameters to determine if the electrical system is affected by theidentified anomalous voltage condition includes comparing the firstparameters to the second parameters to determine if a meaningfuldiscrepancy exists between the first parameters and the secondparameters. In response to determining that a meaningful discrepancyexists between the first parameters and the second parameters, it may bedetermined that the electrical system is affected by the identifiedanomalous voltage condition. Additionally, in response to determiningthat a meaningful discrepancy does not exist between the firstparameters and the second parameters, it may be determined that theelectrical system is not affected by the identified anomalous voltagecondition. In other words, the existence of a meaningful discrepancy (orlack thereof) between the first and second parameters may be used todetermine if an event affected or did not affect the operation of anelectrical system.

In some embodiments, the first parameters are substantially the same asthe second parameters. The first and second parameters may includeenergy-related parameters, for example. In embodiments, theenergy-related parameters may include at least one of voltage, current,energy, active power, apparent power, reactive power, harmonic voltages,harmonic currents, total voltage harmonic distortion, total currentharmonic distortion, harmonic power, individual phase currents,three-phase currents, phase voltages, and line voltages. In embodiments,the energy-related parameters may include (or leverage) substantiallyany electrical parameter derived from the voltage and current signals(including the voltages and currents themselves). In embodiments, thefirst time corresponds to a time prior to the identified voltage event.Additionally, in embodiments the second time corresponds to a time afterthe identified voltage event.

In embodiments, in response to determining that the electrical system isnot affected by the identified anomalous voltage condition, voltagecondition data associated with the identified anomalous voltagecondition may be stored in a memory device associated with the IED. Theidentified anomalous voltage condition may be classified asnon-impactful to the electrical system. In embodiments, in response todetermining that the electrical system is affected by the identifiedanomalous voltage condition, voltage condition data associated with theidentified anomalous voltage condition may be stored in the memorydevice associated with the IED. The identified anomalous voltagecondition may be classified as impactful (i.e., impacting the operationof one or more loads and/or processes) to the electrical system.

In embodiments, the upper alarm thresholds and the lower alarmthresholds are indicated in a dynamic tolerance curve associated withone or more loads monitored by the respective one of the plurality ofIEDs in the electrical system. In some embodiments, the dynamictolerance curve characterizes a response of the one or more loads topower quality events including the anomalous voltage condition.Additionally, in some embodiments the dynamic tolerance curvecharacterizes an impact of the power quality events on the electricalsystem. In embodiments, the dynamic tolerance curve includes at leastone magnitude threshold above the nominal voltage associated with aduration and/or at least one magnitude threshold below the nominalvoltage associated with a duration. In embodiments, the upper alarmthresholds and/or the lower alarm thresholds are adjusted to themeasured IED voltage to more accurately represent true voltage event(i.e., perturbation) sensitivity at the respective one of the pluralityof IEDs point of installation in the electrical system.

In some embodiments, the identified anomalous voltage condition includesa plurality of anomalous voltage conditions. Additionally, in someembodiments the plurality of anomalous voltage conditions areprioritized based on one or more parameters associated with theplurality of anomalous voltage conditions. The parameters may includemagnitude and/or duration of the plurality of anomalous voltageconditions, for example. In embodiments, the magnitude and/or durationof the plurality of anomalous voltage conditions are indicative ofanomalous voltage condition type(s).

In some embodiments, in response to determining that the electricalsystem is affected by the identified anomalous voltage condition, atleast one source of the identified anomalous voltage condition may beidentified. In some embodiments, it may be determined if the at leastone identified source is upstream or downstream from a metering point inthe electrical system associated with the respective one of theplurality of IEDs.

In embodiments, the identified anomalous voltage condition is indicativeof a voltage event. The voltage event may include, for example, one of avoltage sag, a voltage swell, a voltage transient, an instantaneousinterruption, a momentary interruption, a temporary interruption, and along-duration root-mean-square (rms) variation.

In another aspect of this disclosure, a method for managing voltageevent alarms in an electrical system includes processing electricalmeasurement data from, or derived from, energy-related signals capturedby at least one IED of a plurality of IEDs to identify an anomalousvoltage condition at a point of installation of a respective one of theplurality of IEDs in the electrical system. In embodiments, theanomalous voltage condition corresponding to a magnitude and/or durationof a measured IED voltage being above or greater than one or more upperalarm thresholds or below or less than one or more lower alarmthresholds. The method also includes determining if the electricalsystem is affected by the identified anomalous voltage condition. Inembodiments, in response to determining that the electrical system isnot affected by the identified anomalous voltage condition, at least oneof the one or more upper alarm thresholds or at least one of the one ormore lower alarm thresholds is adjusted to the magnitude and duration ofthe measured IED voltage. In some embodiments, the at least one of theone or more upper alarm thresholds or the at least one of the one ormore lower alarm thresholds is adjusted to the magnitude and duration ofthe measured IED voltage such that the magnitude of the measured IEDvoltage corresponds to a voltage magnitude setpoint of the alarmthresholds, and the duration of the measured IED voltage corresponds toa voltage event duration setpoint of the alarm thresholds.

In some embodiments, determining if the electrical system is affected bythe identified anomalous voltage condition includes identifying loss ofload in the electrical system associated with the identified anomalousvoltage condition, and initiating a load loss alarm indicating theidentified loss of load. In some embodiments, the load loss alarm may begenerated, communicated, indicated, stored, analyzed, managed and/orutilized in at least one or more component (e.g., IED, gateway) and/orsystem (e.g., on-site, cloud-based) associated with the electricalsystem, control system, and/or power monitoring system.

In a further aspect of this disclosure, a method for managing voltageevent alarms in an electrical system includes processing electricalmeasurement data from, or derived from, energy-related signals capturedby at least one intelligent electronic device (IED) of a plurality ofIEDs to identify an anomalous voltage condition (e.g., a power qualityissue or event) at a point of installation of a respective one of theplurality of IEDs in the electrical system. The anomalous voltagecondition may correspond, for example, to a measured IED voltage beingabove or greater than one or more upper alarm thresholds or below orless than one or more lower alarm thresholds, for example, as shown inFIGS. 49 and 50. The method also includes determining if the electricalsystem is affected (or “impacted,” as sometimes referred to herein) bythe identified anomalous voltage condition, for example, based, at leastin part, on an evaluation of at least one of a magnitude and a durationof the identified anomalous voltage condition. In response todetermining that the electrical system is affected by the identifiedanomalous voltage condition, at least one of a plurality of criteria maybe chosen to adjust at least one of the upper alarm thresholds and/or atleast one of the lower alarm thresholds.

In accordance with some embodiments of this disclosure, the at least oneof the magnitude and the duration of the identified anomalous voltagecondition (e.g., voltage sag) used in the evaluation to determine if theelectrical system is affected by the identified anomalous voltagecondition corresponds to a measured magnitude and a measured duration ofthe identified anomalous voltage condition. In accordance with someembodiments of this disclosure, at least one of the measured magnitudeand the measured duration of the identified anomalous voltage conditionis compared to at least one of a reference magnitude and a referenceduration to determine if the electrical system is affected by theidentified anomalous voltage condition. In accordance with someembodiments of this disclosure, the reference magnitude and thereference duration are used to determine impact of the identifiedanomalous voltage condition on the electrical system. In response todetermining that the impact of identified anomalous voltage conditionexceeds an impact threshold, for example, it may be determined that partand/or all of the electrical system and/or its loads are affected by theidentified anomalous voltage condition.

In accordance with some embodiments of this disclosure, the chosen atleast one of the plurality of criteria includes recovery time. Inaccordance with some embodiments of this disclosure, the recovery timeis associated with impact of the anomalous voltage condition on theelectrical system. Additionally, in accordance with some embodiments ofthis disclosure, the recovery time corresponds to accumulated recoverytime, the accumulated recovery time associated with recurrence of theanomalous voltage condition over one or more periods of time (e.g.,days, weeks, months, seasons, years, etc.). The accumulated recoverytime may be reset, for example, in response to one or more conditions(e.g., user specified conditions).

In accordance with some embodiments of this disclosure, the chosen atleast one of the plurality of criteria includes the response of one ormore load types to the anomalous voltage condition. Additionally, inaccordance with some embodiments of this disclosure, the chosen at leastone of the plurality of criteria includes input/output (I/O) data. TheI/O data may include, for example, at least one of a digital signal(e.g., two discrete states) and an analog signal (e.g., continuouslyvariable). The digital signal may include, for example, at least one ofon/off status(es), open/closed status(es), high/low status(es),synchronizing pulse and any other representative bi-stable signal.Additionally, the analog signal may include, for example, at least oneof temperature, pressure, volume, spatial, rate, humidity, and any otherrepresentative physically representative signal.

In accordance with some embodiments of this disclosure, the chosen atleast one of the plurality of criteria includes time(s) of occurrence ofthe anomalous voltage condition. In accordance with some embodiments ofthis disclosure, a voltage event alarm or alarms associated with the oneor more upper alarm thresholds and/or the one or more lower alarmthresholds may be configured to be turned on, off, or adjusted (e.g.,increased or decreased) in response to the time(s) of occurrence of theanomalous voltage condition meeting certain criteria. The certaincriteria may include, for example, a time or times at which the voltageevent alarm or alarms are configured to be silenced or muted (e.g., fora given time period).

In accordance with some embodiments of this disclosure, the chosen atleast one of the plurality of criteria includes user configuredcriteria. Additionally, in accordance with some embodiments of thisdisclosure, the chosen at least one of the plurality of criteriaincludes or is based on historical data. The historical data mayinclude, for example, at least one of non-impactful event data andimpactful event data from historical events. The historical events maybe associated with one or more aspects, portions, zones, processes, etc.of the electrical system, for example. In one aspect of this disclosure,the historical events may be grouped and/or archived based on anend-user segment under consideration (i.e., end-user segment associatedwith the electrical system for which voltage event alarms are beingmanaged). The end-user segment under consideration may include, forexample, at least one of: retail, offices, hotels, hospitals, datacenters, food and beverages, oil and gas, industrial, automotive,utility, manufacturing, educational, governmental, residential andcommercial. It is understood that the above-mentioned example end-usersegments are but a few of many possible end-user segments that mayutilize the systems and methods disclosed herein.

In accordance with some embodiments of this disclosure, the chosen atleast one of the plurality of criteria includes a combination ofcriteria (i.e., two or more of the plurality of criteria). Thecombination of criteria may be selected, for example, based on one ormore factors (e.g., user configured or learned factors). The factors mayinclude affiliation of the criteria and determined or learnedeffectiveness of the criteria for evaluating and adjusting thresholds,for example. For example, recovery time, which is one example type ofcriteria, is often related to impact, which is another example type ofcriteria. Accordingly, in accordance with some embodiments of thisdisclosure, it may make most sense to choose recovery time and impactsas the at least one of the plurality of criteria. Additionally, withrespect to determined or learned effectiveness of the criteria forevaluating and adjusting thresholds, it may be determined or learnedover time which combinations of criteria are more effective forevaluating and adjusting thresholds, and which combinations of criteriaare less effective for evaluating and adjusting thresholds. Examplecombinations of criteria may include, for example, at least two of:segment context, recovery energy, emissions volume (anticipated CO₂emissions), product losses, cost(s), load type(s), and any other numberof criteria as disclosed herein and apparent to one of ordinary skill inthe art.

In accordance with some embodiments of this disclosure, the amount(s)and/or level(s) by which the at least one of the upper alarm thresholdsand/or the at least one of the lower alarm thresholds is/are adjustedis/are based on an evaluation of at least one characteristic associatedwith the identified anomalous voltage condition with respect to at leastone of the plurality of criteria (e.g., impact as a first criteria,recovery time as a second criteria, production losses as a thirdcriteria, time as a fourth criteria, etc.). The at least onecharacteristic associated with the identified anomalous voltagecondition may include, for example, at least one of the magnitude andthe duration of the identified anomalous voltage condition.

In accordance with some embodiments of this disclosure, the upper alarmthresholds and the lower alarm thresholds are indicated in a dynamictolerance curve associated with one or more loads monitored by therespective one of the plurality of IEDs in the electrical system. Inaccordance with some embodiments of this disclosure, the dynamictolerance curve characterizes response of the one or more loads to powerquality events including the identified anomalous voltage condition, andan impact of the power quality events on the electrical system, based onand/or responsive to the at least one of a plurality of criteria. Thedynamic tolerance curve may include, for example, a magnitude thresholdabove the nominal voltage associated with a duration and a magnitudethreshold below the nominal voltage associated with a duration. Themagnitude threshold above the nominal voltage and the magnitudethreshold below the nominal voltage may be based on and/or responsive tothe at least one of a plurality of criteria, for example. In accordancewith some embodiments of this disclosure, the at least one dynamictolerance curve includes a plurality of dynamic tolerance curves, theplurality of dynamic tolerance curves including at least a first dynamictolerance curve for a first day or time period and a second dynamictolerance curve for a second day or time period.

In accordance with some embodiments of this disclosure, the aforesaidmethod (e.g., further illustrated by FIGS. 55-57, as will be discussedfurther below) may be used to initially set or adjust the at least oneof the upper alarm thresholds and/or the at least one of the lower alarmthresholds (e.g., magnitude, duration). Additionally, in accordance withsome embodiments of this disclosure, the aforesaid method may be used torefine or readjust or further constrain the at least one of the upperalarm thresholds and/or the at least one of the lower alarm thresholdsafter these thresholds are initially set or adjusted using at least oneof the previously discussed methods (e.g., further illustrated by FIGS.51-54, as will be discussed further below), for example. In accordancewith some embodiments of this disclosure, this may further reduce thenumber of alarms a system operator, etc. may see.

Systems for managing voltage event alarms in an electrical system arealso disclosed herein. In one aspect of this disclosure, a system formanaging voltage event alarms in an electrical system includes at leastone processor and at least one memory device coupled to the at least oneprocessor. The at least one processor and the at least one memory devicemay be configured to process electrical measurement data from, orderived from, energy-related signals captured by at least one IED of aplurality of IEDs to identify an anomalous voltage condition at a pointof installation of a respective one of the plurality of IEDs in theelectrical system. The anomalous voltage condition may correspond, forexample, to a measured IED voltage being above or greater than one ormore upper alarm thresholds or below or less than one or more loweralarm thresholds. The at least one processor and the at least one memorydevice may also be configured to determine if the electrical system isaffected by the identified anomalous voltage condition, for example,based, at least in part, on an evaluation of at least one of a magnitudeand a duration of the identified anomalous voltage. In response todetermining that the electrical system is affected by the identifiedanomalous voltage condition, at least one of a plurality of criteria maybe chosen to adjust at least one of the upper alarm thresholds and/or atleast one of the lower alarm thresholds.

In another aspect of this disclosure, a system for managing voltageevent alarms in an electrical system includes at least one IED includingat least one processor and at least one memory device coupled to theprocessor. The at least one processor and the at least one memory deviceare configured to process electrical measurement data from, or derivedfrom, energy-related signals captured by the at least one IED toidentify an anomalous voltage condition at a point of installation ofthe at least one IED in the electrical system. In embodiments, theanomalous voltage condition corresponds to a measured IED voltage beingabove or greater than one or more upper alarm thresholds or below orless than one or more lower alarm thresholds. The at least one processorand the at least one memory device are also configured to determine ifthe electrical system is affected by the identified anomalous voltagecondition. In embodiments, in response to determining that theelectrical system is not affected by the identified anomalous voltagecondition, at least one of the one or more upper alarm thresholds or atleast one of the one or more lower alarm thresholds is adjusted to themeasured IED voltage.

In some embodiments, the at least one processor and the at least onememory device of the at least one IED are further configured to identifyloss of load in the electrical system associated with the identifiedanomalous voltage condition, and initiate a load loss alarm indicatingthe identified loss of load. In some embodiments, the load loss alarmmay be generated, communicated, indicated, stored, analyzed, managedand/or utilized in at least one or more component (e.g., IED, gateway)and/or system (e.g., on-site, cloud-based) associated with theelectrical system, control system, and/or power monitoring system.

Alternatively, the at least IED may capture voltage and/or currentsignals (or waveforms) and forward the waveforms to another processor toevaluate for impact. The other processor may be located on-site(gateway, on-site software, IED, etc.) or off-site (cloud-based, remotesoftware, etc.).

As will be further understood from discussions below, an importantadvantage of characterizing the electrical system's voltage eventtolerance at a metering/IED point is to customize alarm thresholds atthe meter's point of installation. Using dynamic voltage eventcharacterization to manage alarms provides several benefits includingensuring 1) relevant events are captured, 2) excessive alarms areprevented, 3) appropriate alarms are configured, and 4) important alarmsare prioritized.

As is known, existing approaches to alarm configuration and managementtypically require manual configuration by an end-user based on standards(e.g., IEEE standards), recommendations, or guessing. As is also known,existing approaches to alarm configuration and management typicallyrequire some form of setpoint learning that necessitates a configuration“learning period” to determine what is normal. Unfortunately, however,if an event occurs during the learning period, it is considered normalbehavior unless the end-user caught it or suggested otherwise. Moreover,setpoint learning is typically based on a voltage event's magnitudealone, and doesn't include any form of event impact analysis. Existingapproaches may also use a “capture everything” approach that requiresthe end-user to apply filters to distinguish which alarms are importantand which are not. Again, a “capture everything” approach does notgenerally disaggregate an impactful event from a non-impactful event. Inshort, the end-user (which may not be an expert) is required to activelydiscriminate which event alarms/thresholds are important, either beforeor after the event alarms are captured.

In some embodiments, the at least one IED and the loads of the above andbelow discussed systems and methods are installed at a same respectivelocation or metering points in the electrical system. Additionally, insome embodiments the at least one IED and the loads of the above-method(and below described systems and methods) are installed at differentrespective locations (i.e., a plurality of locations) or metering pointsin the electrical system. In embodiments in which the electrical systemincludes more than one load, for example, a specific IED may be upstreamfrom one load in the electrical system while being downstream fromanother load in the electrical system.

As used herein, the terms “upstream” and “downstream” are used to referto electrical locations within an electrical system. More particularly,the electrical locations “upstream” and “downstream” are relative to anelectrical location of an IED collecting data and providing thisinformation. For example, in an electrical system including a pluralityof IEDs, one or more IEDs may be positioned (or installed) at anelectrical location that is upstream relative to one or more other IEDsin the electrical system, and the one or more IEDs may be positioned (orinstalled) at an electrical location that is downstream relative to oneor more further IEDs in the electrical system. A first IED or load thatis positioned on an electrical circuit upstream from a second IED orload may, for example, be positioned electrically closer to an input orsource of the electrical system (e.g., a utility feed) than the secondIED or load. Conversely, a first IED or load that is positioned on anelectrical circuit downstream from a second IED or load may bepositioned electrically closer to an end or terminus of the electricalsystem than the other IED.

A first IED or load that is electrically connected in parallel (e.g., onan electrical circuit) with a second IED or load may be considered to be“electrically” upstream from said second IED or load in embodiments, andvice versa. In embodiments, algorithm(s) used for determining adirection of a power quality event (i.e., upstream or downstream) is/arelocated (or stored) in the IED, cloud, on-site software, gateway, etc.As one example, the IED can record an electrical event's voltage andcurrent phase information (e.g., by sampling the respective signals) andcommunicatively transmit this information to a cloud-based system. Thecloud-based system may then analyze the voltage and current phaseinformation (e.g., instantaneous, root-mean-square (rms), waveformsand/or other electrical characteristic) to determine if the source ofthe voltage event was electrically upstream or downstream from where theIED is electrically coupled to the electrical system (or network).

In some embodiments, the electrical measurement data from, or derivedfrom, energy-related signals captured by the at least one IED may beprocessed on the at least one IED, as in the above-described system formanaging voltage event alarms in an electrical system, or be processedin on-site software, in a cloud-based application, or in a gateway,etc., to manage voltage event alarms. Additionally, in some embodimentsthe electrical measurement data may be processed on a control systemassociated with the electrical system to manage voltage event alarms.The control system may be used for controlling one or more parametersassociated with the electrical system, for example. In embodiments,identifying the anomalous voltage condition may include identifying: (a)a type of power quality event associated with the anomalous voltagecondition, (b) a magnitude of the anomalous voltage condition, (c) aduration of the anomalous voltage condition, and/or (d) a location ofthe anomalous voltage condition in the electrical system. Inembodiments, the power quality event type may include one of a voltagesag, a voltage swell, and a voltage transient. Additionally, inembodiments the location of the anomalous voltage condition may bederived from voltage and current signals as measured by the IEDs andassociated with the anomalous voltage condition.

As discussed above, an anomalous voltage condition may be indicative ofa voltage event. As also discussed above, a voltage event is one exampletype of power quality event. A power quality event may include at leastone of a voltage sag, a voltage swell, and a voltage transient, forexample. According to IEEE Standard 1159-2019, for example, a voltagesag is a decrease to between 0.1 and 0.9 per unit (pu) in rms voltage orcurrent at the power frequency for durations of 0.5 cycle to 1 min.Typical values are 0.1 to 0.9 pu. Additionally, according to IEEEStandard 1159-2019, a voltage swell is an increase in rms voltage orcurrent at the power frequency for durations from 0.5 cycles to 1 min.Below is a table from IEEE Standard 1159-2019 (known art), which definesvarious categories and characteristics of power system electromagneticphenomena.

Typical Typical Typical Categories spectral content duration voltagemagnitude 1.0 Transients 1.1 Impulsive 1.1.1 Nanosecond 5 ns rise <50 ns1.1.2 Microsecond 1 μs rise 50 ns-1 ms 1.1.3 Millisecond 0.1 ms rise >1ms 1.2 Oscillatory 1.2.1 Low frequency <5 kHz 0.3-50 ms 0-4 pu^(a) 1.2.2Medium frquency 5-500 kHz 20 μs 0-8 pu 1.2.3 High frequency 0.5-5 MHz 5μs 0-4 pu 2.0 Short-duration root- mean-square (rms) variations 2.1Instantaneous 2.1.1 Sag 0.5-30 cycles 0.1-0.9 pu 2.1.2 Swell 0.5-30cycles 1.1-1.8 pu 2.2 Momentary 2.2.1 Interruption 0.5 cycles-3 s  <0.1pu 2.2.2 Sag 30 cycles-3 s 0.1-0.9 pu 2.2.3 Swell 30 cycles-3 s 1.1-1.4pu 2.2.4 Voltage Imbalance 30 cycles-3 s 2%-15% 2.3 Temporary 2.3.1Interruption >3 s-1 min <0.1 pu 2.3.2 Sag >3 s-1 min 0.1-0.9 pu 2.3.3Swell >3 s-1 min 1.1-1.2 pu 2.3.4 Voltage Imbalance >3 s-1 min 2%-15%3.0 Long duration rms variations 3.1 Interruption, sustained >1 min 0.0pu 3.2 Undervoltages >1 min 0.8-0.9 pu 3.3 Overvoltages >1 min 1.1-1.2pu 3.4 Current overload >1 min 4.0 Imbalance 4.1 Voltage steady state0.5-5% 4.2 Current steady state 1.0-3.0%   5.0 Waveform distortion 5.1DC offset steady state 0-0.1% 5.2 Harmonics 0-9 kHz steady state  0-20%5.3 Interharmonics 0-9 kHz steady state   0-2% 5.4 Notching steady state5.5 Noise broadband steady state   0-1% 6.0 Voltage flucuations <25 Hzintermittent 0.1-7% 0.2-2 P_(st) ^(b) 7.0 Power frequency variations <10s ±0.10 Hz NOTE- These terms and categories apply to power qualitymeasurements and are not to be confused with similar terms defined inIEEE Std 1366 ™-2012 [B30] and other reliability-related standards,recommended practices, and guides. ^(a)The quanity pu refers to perunit, which is demensionless. The quanity 1.0 pu corresponds to 100%.The nominal condition is often considered to be 1.0 pu. In this table,the nominal peak value is used as the base for transients and nominalrms value is used as the base for rms variations. ^(b)Flicker severityindex P_(st) as defined in IEC 61000-4-15-2010 [B17] and IEEE Std 1453 ™[B31].

It is understood that the above table is one standards body's (IEEE inthis case) way of defining/characterizing power quality events. It isunderstood there are other standards that define power qualitycategories/events as well, such as the International ElectrotechnicalCommission (IEC), American National Standards Institute (ANSI), etc.,which may have different descriptions or power quality event types,characteristics, and terminology. In embodiments, power quality eventsmay be customized power quality events (e.g., defined by a user).

In some embodiments, the electrical measurement data processed toidentify the anomalous voltage condition (and power quality events) maybe continuously or semi-continuously captured by the at least one IED,and the tolerance curve may be dynamically updated in response toanomalous voltage conditions (or power quality events) detected (oridentified) from the electrical measurement data. For example, thetolerance curve may initially be generated in response to power qualityevents identified from electrical measurement data captured at a firsttime, and may be updated or revised in response to (e.g., to include orincorporate) power quality events identified from electrical measurementdata captured at a second time. As events are captured, the tolerancecurve (also sometimes referred to herein as “a dynamic tolerance curve”)may be continuously (e.g., dynamically) updated according to the uniqueresponse of the electrical system.

In some embodiments, the tolerance curve may be displayed in a graphicaluser interface (GUI) of the at least one IED, or the GUI of a controlsystem used for monitoring or controlling one or more parametersassociated with the electrical system. In embodiments, the controlsystem may be a meter, an IED, on-site/head-end software (i.e., asoftware system), a cloud-based control system, a gateway, a system inwhich data is routed over the Ethernet or some other communicationssystem, etc. A warning may be displayed in the GUI of the IED, themonitoring system or the control system, for example, in response to adetermined impact (or severity) of the anomalous voltage condition (orpower quality event) being outside of a range or threshold (e.g.,voltage event alarm threshold). In some embodiments, the range is apredetermined range, for example, a user configured range. Additionally,in some embodiments the range is automatic, for example, usingstandards-based thresholds. Further, in some embodiments the range is“learned,” for example, by starting with a nominal voltage and pushingout the thresholds as non-impactful events occur in the natural courseof the electrical network's operation.

The GUI may be configured to display factors contributing to theanomalous voltage condition (or power quality event). Additionally, theGUI may be configured to indicate a location of the anomalous voltagecondition (or power quality event) in the electrical system. Further,the GUI may be configured to indicate how the loads (or another specificsystem or piece of equipment in the electrical system) will respond tothe anomalous voltage condition (or power quality event). It isunderstood that any number of information may be displayed in the GUI.As part of this invention, any electrical parameter, impact to aparameter, I/O status input, I/O output, process impact, recovery time,time of impact, phases impacted, potentially discrete loads impactedbeneath a single IED, etc. may be displayed in the GUI. FIG. 20, forexample, as will be discussed further below, shows a simple example ofincorporating percent load impacted with an indication of recovery time.

In embodiments, the tolerance curve displayed in the GUI does not havefixed scaling but, rather, can (and needs to) auto-scale, for example,to capture or display a plurality of power quality events. In accordancewith various aspects of the disclosure, the beauty of having a dynamictolerance curve is not being constrained to a static curve or curves(e.g., with fixed scaling). For example, referring briefly to FIG. 2(which will be discussed further below), while the y-axis is shown as apercent of nominal in FIG. 2, it can also be shown as an absolutenominal value (e.g., 120 volts, 208 volts, 240 volts, 277 volts, 480volts, 2400 volts, 4160 volts, 7.2 kV, 12.47 kV, etc.). In this case,auto-scaling would be required because different voltage ranges wouldrequire different scaling for the y-axis. Additionally, the x-axis maybe scaled in different units (e.g., cycles, seconds, etc.) and/or mayhave a variable maximum terminus point (e.g., 10 seconds, 1 minute, 5minutes, 600 cycles, 3600 cycles, 18,000 cycles, etc.). In other words,in some embodiments there is no reason for the GUI to show more than ithas to.

In embodiments, a goal of the invention claimed herein is to build acustomized tolerance curve for a discrete location within a customer'spower system (e.g., at a given IED) based on a perceived impact todownstream loads. Additionally, in embodiments a goal of the inventionclaimed herein is to quantify the time it takes to recover from a powerquality event. In short, aspects of the invention claimed herein aredirected toward describing the impact of a power quality event, whichallows a customer to understand their operational parameters andconstraints, accordingly.

The features proposed in this disclosure evaluate specific power qualityevents to quantify their impact on loads of an electrical system,recovery time, and other useful or interesting parameters. Its scope mayinclude discrete metered points, network zones, and/or the aggregatedelectrical system in total. Novel ideas to display these concepts arealso discussed, allowing the energy consumer to more efficiently andcost-effectively identify, analyze, mitigate, and manage theirelectrical networks.

Of the seven recognized power quality categories defined by IEEE1159-2019, short-duration root mean square (rms) variations aregenerally the most disruptive and have the largest universal economicimpact on energy consumers. Short-duration rms variations includevoltage sags/dips, swells, instantaneous interruptions, momentaryinterruptions and temporary interruptions. One example study by theElectric Power Research Institute (EPRI) estimates an average of about66 voltage sags are experienced by industrial customers each year. Asthe trend of industries becoming more dependent on sag-sensitiveequipment has increased, so has the impact of these events.

The prevalence of voltage sags and the consequences of a growing installbase of sag-sensitive equipment present many additional opportunitiesfor electric solutions and services providers. The table belowillustrates several example opportunities:

Opportunities Benefits Solutions Increased monitoring systems componentsMore suitable sag-immunity equipment Targeted sag mitigation equipmentServices Engineering and consulting Remote monitoring and diagnosticsEquipment installation

As is known, a primary purpose of metering system alarms is to indicatewhen a threshold has been exceeded. Exceeding alarm thresholds are oftenthe result of an anomalous electrical system condition, and the alarmacts as a warning of this condition. Metering systems have a history ofproviding excessive alarms, especially when the alarm thresholds are notconfigured properly. These excessive alarms (sometimes referred to asnuisance alarms or “alarm avalanche”) inundate users with warningsregarding their system and can lead to confusion (best case scenario)and simply disregarding feedback from their power monitoring systemaltogether (worst case scenario).

As further discussed in the Detailed Description section of thisdisclosure, it is possible/preferable to make alarms more productive byconfiguring them based on voltage events that actually impact theend-user's systems. In accordance with some embodiments of thisinvention, if an alarm is generated and the system has not been impacted(determined based on a pre-event vs. post-event change in theenergy/power consumption), at least one alarm threshold is tightened tomatch the characteristics of said voltage event. This continues until avoltage event occurs with a coincident impact to the energy/powerconsumption.

Because each end-user has different tolerances for a system impact dueto voltage events, it is possible to use other relevant characteristicsand repercussions associated with said voltage events to fine-tune thealarm thresholds so that nuisance alarms will be reduced. This is trueeven in the case where a voltage event produces an energy/power impact.For example, some end-users may experience “brief” impactful voltageevents that they wish to suppress. For instance, a commercial officebuilding may experience a brief voltage sag event from the utilityresulting in an interruption to the building. The equipment and systemswithin the building may automatically reset upon reapplying the sourcevoltage with little to no impact to their operation. The facilitymanager for the building may want to ignore and/or reduce the priorityof the impact associated with the voltage event.

The principal element of this feature is to allow end-users to betterconfigure alarm thresholds to filter impactful events based onadditional criteria (i.e., a subset of impactful voltage events). Thereare numerous event criteria that can be used/leveraged to help configurealarm thresholds. For example, a voltage event producing an impact tothe electrical system at a given location within the system may havesome recovery time (recovery period), ‘t_(r)’. The end-user may wish toignore events where t_(r)≤5 minutes. By analyzing non-impactful andimpactful event data from historical events, it is possible to determinethe optimal alarm thresholds and configure the appropriate IEDs toignore (or alternatively, provide at a lower priority) voltage eventswith a recovery time less than or equal to 5 minutes. An appropriatedynamic tolerance curve (DTC) may be created and/or updated to reflectthe optimal alarm thresholds for voltage events to satisfy the t_(r)constraint.

It is important to note that absolute magnitude (amplitude), relativepercent (of some nominal value), and/or duration (time) may beconsidered, configured and/or adjusted to provide the optimal alarmthreshold(s). Tolerance curves may be dynamicallycreated/adjusted/readjusted/etc. to reflect the optimal alarm thresholdsand/or priorities to achieve the desired alarm suppression (orconversely, the desired alarm expression). This DTC will likely exhibitaltered, more moderate (e.g., looser) threshold settings than a dynamictolerance curve based only on energy/power impacts due to the reasonsmentioned above. It is possible to illustrate two overlapping DTCs toend-users: the first to describe only the impact sensitivitycharacteristics due to voltage events at the measuring IED's location,and the second to describe to describe the more liberal characteristicsdesired by the end-user as measured at said IED's location. Moreover, itis possible to have any number of overlapping DTCs to reflect multipleconsiderations describing the other alarm threshold constraints.

For example, it is possible to set alarm thresholds to describe thefollowing:

-   -   a particular amount of load impacted, either by absolute value        (e.g., 10 kW) or relative; percentage of nominal (e.g., 10% of        the average load);    -   a particular zone, process and/or IED is impacted;    -   a particular level of production losses;    -   a particular location of the event (upstream/upline or        downstream/downline);    -   a particular cost (e.g., manufacturing, business, or        intangible);    -   a particular change in some parameter (i.e., electrical,        mechanical, I/O, etc.); and    -   Or by some combination or amalgamation of the aforementioned        conditions.

Configuration/reconfiguration of alarm thresholds to reflect the ideasherein may also be limited to events either above or below the nominalvoltage. For example, the feature may be configured to only change alarmthresholds above the nominal voltage to reflect concepts described inthis document while having alarm thresholds below the nominal voltagereflect any impact associated with a voltage event (and vice versa).

As will be further appreciated from discussions below, the disclosedsystems and methods provide versatility to automaticallyconfigure/reconfigure alarm thresholds that best mirror the end-user'sintentions and limit/constrain alarms from their metering/monitoringsystem. Because all end-users have different ‘pain points’, differentneeds/usages, and different benefits derived from theirmetering/monitoring system, it is important to allow autonomy to choosehow alarms are suppressed with respect to the specific system beingmonitored.

It is understood that there are many other features and advantagesassociated with the disclosed invention, as will be appreciated from thediscussions below.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing features of the disclosure, as well as the disclosureitself may be more fully understood from the following detaileddescription of the drawings, in which:

FIG. 1 shows a graphical view of several example power qualitycategories;

FIG. 1A shows an example electrical system in accordance withembodiments of the disclosure;

FIG. 1B shows an example intelligent electronic device (IED) that may beused in an electrical system in accordance with embodiments of thedisclosure;

FIG. 2 shows an example Information Technology Industry (ITI) curve(also sometimes referred to as a “power acceptability curve”);

FIG. 3 shows an example baseline voltage tolerance curve which could bethe ITI curve (as illustrated) or some other unique relationship betweenan event's voltage magnitude and duration;

FIG. 4 shows an example voltage sag event on a baseline voltagetolerance curve;

FIG. 5 shows an example recommended change to the baseline voltagetolerance curve of FIG. 3 based on an impact of the voltage sag eventshown in FIG. 4;

FIG. 6 shows an example dynamically customized and updated voltagetolerance curve;

FIG. 7 shows an example of a multitude of impactful and non-impactfulvoltage sags, swells, and transients on a voltage tolerance curve;

FIG. 8 shows a dynamically customized and updated voltage tolerancecurve for a multitude of impactful and non-impactful events;

FIG. 9 shows an example three-dimensional (3-D) tolerance-impact curvewith load(s) impact;

FIG. 10 shows an example 3-D tolerance-impact curve with gradient colorshading indicating severity of load(s) impact;

FIG. 11 shows an example 3-D tolerance-impact curve with a sample eventindicating severity of load(s) impact;

FIG. 12 shows an example 3-D tolerance-impact curve with recovery time;

FIG. 13 shows an example 3-D tolerance-impact curve with gradient colorshading indicating length of recovery time;

FIG. 14 shows an example 3-D tolerance-impact curve with a sample eventindicating length of recovery time;

FIG. 15 shows another example 3-D tolerance-impact curve with a sampleevent indicating production losses as an economic impact;

FIG. 16 shows an example simple electrical network with a fault;

FIG. 16A shows another example electrical network with a fault;

FIG. 17 shows an example customized tolerance curve with a multitude ofimpactful and non-impactful upstream and downstream events;

FIG. 18 shows an example customized tolerance curve with a multitude ofimpactful and non-impactful disaggregated upstream events;

FIG. 19 shows an example customized tolerance curve with a multitude ofimpactful and non-impactful disaggregated downstream events;

FIG. 20 shows an example 3-D tolerance-impact curve with load impact,recovery time and upstream/downstream event sources indicated for amultitude of events;

FIG. 21 is a diagram showing an example progression of costs to mitigatevoltage events;

FIG. 22 shows an example customized and updated tolerance curve for thevoltage sag event illustrated in FIG. 4;

FIG. 23 shows the SEMI F47 curve superimposed on the plot illustrated inFIG. 22;

FIG. 24 shows example ride-through benefits of a sag mitigation devicein an electrical system, one example of which is SagFighter® bySchneider Electric;

FIG. 25 shows an example of a multitude of potentially avoided loadimpact events with a sag mitigation device;

FIG. 26 shows another example of a multitude of potentially avoided loadimpacting events and their aggregated recovery time with a sagmitigation device;

FIG. 27 shows an example of the predicted impact of installing a voltageevent mitigation device;

FIG. 28 shows an example of the actual impact of installing a voltageevent mitigation device;

FIG. 29 shows an example of a simple electrical system with a pluralityof IEDs;

FIG. 30 shows an example recovery timeline for a plurality of IED typesexperiencing a voltage event;

FIG. 30A illustrates an example of virtual metering being used toidentify an impact of a voltage event on unmetered loads;

FIG. 30B shows an example electrical system in accordance withembodiments of this disclosure;

FIGS. 30C-30E show example dynamic tolerance curves in accordance withembodiments of this disclosure;

FIG. 30E-30I show further example electrical systems in accordance withembodiments of this disclosure;

FIG. 31 shows an example fault on the simple electrical system of FIG.29;

FIG. 32 shows example zones of the simple electrical system of FIG. 29,for example, based on step-down transformer locations;

FIG. 33 shows an example customized zone configuration of the simpleelectrical system of FIG. 29;

FIG. 34 shows an example of a simple voltage tolerance curve (alsosometimes referred to as a power acceptability curve);

FIG. 35 shows an example voltage sag event shown on the simple voltagetolerance curve of FIG. 34;

FIG. 36 shows an example updated voltage tolerance curve after thevoltage sag event illustrated in FIG. 35;

FIG. 37 shows an example second voltage sag event on the voltagetolerance curve illustrated in FIG. 36;

FIG. 38 shows an example updated voltage tolerance curve after thesecond voltage sag event illustrated in FIG. 37;

FIG. 39 shows a third example voltage sag event on the voltage tolerancecurve illustrated in FIG. 38;

FIG. 40 shows an example voltage tolerance curve after the third voltagesag event illustrated in FIG. 39;

FIG. 41 is a plot showing measured load(s) versus time for an exampleimpactful voltage event;

FIG. 42 is a plot showing measured load(s) versus time for multipleexample impactful voltage events;

FIG. 43 is a plot showing measured, typical and expected load(s) versustime for an example voltage event;

FIG. 44 is a plot showing percent load impact versus time;

FIG. 45 is a flowchart illustrating an example method for managing powerquality events (or disturbances) in an electrical system;

FIG. 46 is a flowchart illustrating an example method for quantifyingpower quality events (or disturbances) in an electrical system;

FIG. 47 is a flowchart illustrating an example method for expandedqualified lead generation for power quality;

FIG. 48 is a flowchart illustrating an example method for generating adynamic tolerance curve for power quality;

FIG. 49 shows an illustrative waveform;

FIG. 50 shows another illustrative waveform;

FIG. 51 is a flowchart illustrating an example method for managingvoltage event alarms in an electrical system;

FIG. 52 is a flowchart illustrating an example method for determining ifan electrical system is affected by an anomalous voltage condition;

FIG. 53 is a flowchart illustrating another example method fordetermining if an electrical system is affected by an anomalous voltagecondition;

FIG. 54 is a flowchart illustrating an example method for prioritizingvoltage event alarms;

FIG. 55 is a flowchart illustrating another example method for managingvoltage event alarms in an electrical system;

FIG. 56 is a flowchart illustrating an example method for adjusting andvalidating voltage event alarm threshold(s); and

FIG. 57 is an example dynamic tolerance curve illustrating variouscharacteristics associated with anomalous voltage conditions inaccordance with embodiments of this disclosure.

DETAILED DESCRIPTION

The features and other details of the concepts, systems, and techniquessought to be protected herein will now be more particularly described.It will be understood that any specific embodiments described herein areshown by way of illustration and not as limitations of the disclosureand the concepts described herein. Features of the subject matterdescribed herein can be employed in various embodiments withoutdeparting from the scope of the concepts sought to be protected.

For convenience, certain introductory concepts and terms used in thespecification (and adopted from IEEE Standard 1159-2019) are collectedhere. Several of these concepts and terms are shown in FIG. 1, forexample. It is notable that FIG. 1 does not include all power qualitycategories such as waveform distortion, imbalance, voltage fluctuations,and power frequency deviations.

As used herein, the term “aperiodic event” is used to describe anelectrical event that occurs non-cyclically, arbitrarily or withoutspecific temporal regularity. For the sake of this paper, bothshort-duration root-mean-square (rms) variations and transients areconsidered to be aperiodic events (i.e., notching is considered as aharmonic phenomenon).

As used herein, the term “instantaneous interruption” is used todescribe a deviation to 0-10% of the nominal value for a duration of ½cycle to 30 cycles.

As used herein, the term “momentary interruption” is used to describe adeviation to 0-10% of the nominal value for a duration of 30 cycles to 3seconds.

As used herein, the term “sag” (of which a “voltage sag” is one example)is used to describe a deviation to 10-90% of the nominal value, forexample, for a duration of ½ cycle to 1 minute, as shown in FIG. 1.

As used herein, the term “short-duration rms variations” is used todescribe a deviation from the nominal value with a duration of ½ cycleto 1 minute. Sub-categories of short-duration rms variations includeinstantaneous interruptions, momentary interruptions, temporaryinterruptions, sags and swells.

As used herein, the term “swell” is used to describe a deviation greaterthan 110% of the nominal value, for example, for a duration of ½ cycleto 1 minute, as shown in FIG. 1.

As used herein, the term “temporary interruption” is used to describe adeviation to 0-10% of the nominal value for a duration of 3 seconds to 1minute.

As used herein, the term “transient” is used to describe a deviationfrom the nominal value with a duration less than 1 cycle. Sub-categoriesof transients include impulsive (uni-direction polarity) and oscillatory(bi-directional polarity) transients.

In embodiments, the degree of impact a short-duration rms variation hason an energy consumer's facility is primarily dependent on four factors:

-   -   1. The nature and source of the event,    -   2. The susceptibility of the load(s) to the event,    -   3. The event's influence on the process or activity, and    -   4. The cost sensitivity to this event.

Consequently, each customer system, operation or load may responddifferently to a given electrical perturbation. For example, it ispossible for a voltage sag event to significantly impact one customer'soperation while the same voltage sag may have little or no noticeableimpact on another customer's operation. It is also possible for avoltage sag to impact one part of a customer's electrical systemdifferently than it does another part of the same electrical system.

Referring to FIG. 1A, an example electrical system in accordance withembodiments of the disclosure includes one or more loads (here, loads111, 112, 113, 114, 115) and one or more intelligent electronic devices(IEDs) (here, IEDs 121, 122, 123, 124) capable of sampling, sensing ormonitoring one or more parameters (e.g., power monitoring parameters)associated with the loads. In embodiments, the loads 111, 112, 113, 114,115 and IEDs 121, 122, 123, 124 may be installed in one or morebuildings or other physical locations or they may be installed on one ormore processes and/or loads within a building. The buildings maycorrespond, for example, to commercial, industrial or institutionalbuildings.

As shown in FIG. 1A, the IEDs 121, 122, 123, 124 are each coupled to oneor more of the loads 111, 112, 113, 114, 115 (which may be located“upstream” or “downstream” from the IEDs in some embodiments). The loads111, 112, 113, 114, 115 may include, for example, machinery orapparatuses associated with a particular application (e.g., anindustrial application), applications, and/or process(es). The machinerymay include electrical or electronic equipment, for example. Themachinery may also include the controls and/or ancillary equipmentassociated with the equipment.

In embodiments, the IEDs 121, 122, 123, 124 may monitor and, in someembodiments, analyze parameters (e.g., energy-related parameters)associated with the loads 111, 112, 113, 114, 115 to which they arecoupled. The IEDs 121, 122, 123, 124 may also be embedded within theloads 111, 112, 113, 114, 115 in some embodiments. According to variousaspects, one or more of the IEDs 121, 122, 123, 124 may be configured tomonitor utility feeds, including surge protective devices (SPDs), tripunits, active filters, lighting, IT equipment, motors, and/ortransformers, which are some examples of loads 111, 112, 113, 114, 115,and the IEDs 121, 122, 123, 124 may detect ground faults, voltage sags,voltage swells, momentary interruptions and oscillatory transients, aswell as fan failure, temperature, arcing faults, phase-to-phase faults,shorted windings, blown fuses, and harmonic distortions, which are someexample parameters that may be associated with the loads 111, 112, 113,114, 115. The IEDs 121, 122, 123, 124 may also monitor devices, such asgenerators, including input/outputs (I/Os), protective relays, batterychargers, and sensors (for example, water, air, gas, steam, levels,accelerometers, flow rates, pressures, and so forth).

According to another aspect, the IEDs 121, 122, 123, 124 may detectovervoltage and undervoltage conditions, as well as other parameterssuch as temperature, including ambient temperature. According to afurther aspect, the IEDs 121, 122, 123, 124 may provide indications ofmonitored parameters and detected conditions that can be used to controlthe loads 111, 112, 113, 114, 115 and other equipment in the electricalsystem in which the loads 111, 112, 113, 114 and IEDs 121, 122, 123, 124are installed. A wide variety of other monitoring and/or controlfunctions can be performed by the IEDs 121, 122, 123, 124, and theaspects and embodiments disclosed herein are not limited to IEDs 121,122, 123, 124 operating according to the above-mentioned examples.

It is understood that the IEDs 121, 122, 123, 124 may take various formsand may each have an associated complexity (or set of functionalcapabilities and/or features). For example, IED 121 may correspond to a“basic” IED, IED 122 may correspond to an “intermediate” IED, and IED123 may correspond to an “advanced” IED. In such embodiments,intermediate IED 122 may have more functionality (e.g., energymeasurement features and/or capabilities) than basic IED 121, andadvanced IED 123 may have more functionality and/or features thanintermediate IED 122. For example, in embodiments IED 121 (e.g., an IEDwith basic capabilities and/or features) may be capable of monitoringinstantaneous voltage, current energy, demand, power factor, averagesvalues, maximum values, instantaneous power, and/or long-duration rmsvariations, and IED 123 (e.g., an IED with advanced capabilities) may becapable of monitoring additional parameters such as voltage transients,voltage fluctuations, frequency slew rates, harmonic power flows, anddiscrete harmonic components, all at higher sample rates, etc. It isunderstood that this example is for illustrative purposes only, andlikewise in some embodiments an IED with basic capabilities may becapable of monitoring one or more of the above energy measurementparameters that are indicated as being associated with an IED withadvanced capabilities. It is also understood that in some embodimentsthe IEDs 121, 122, 123, 124 each have independent functionality.

In the example embodiment shown, the IEDs 121, 122, 123, 124 arecommunicatively coupled to a central processing unit 140 via the “cloud”150. In some embodiments, the IEDs 121, 122, 123, 124 may be directlycommunicatively coupled to the cloud 150, as IED 121 is in theillustrated embodiment. In other embodiments, the IEDs 121, 122, 123,124 may be indirectly communicatively coupled to the cloud 150, forexample, through an intermediate device, such as a cloud-connected hub130 (or a gateway), as IEDs 122, 123, 124 are in the illustratedembodiment. The cloud-connected hub 130 (or the gateway) may, forexample, provide the IEDs 122, 123, 124 with access to the cloud 150 andthe central processing unit 140.

As used herein, the terms “cloud” and “cloud computing” are intended torefer to computing resources connected to the Internet or otherwiseaccessible to IEDs 121, 122, 123, 124 via a communication network, whichmay be a wired or wireless network, or a combination of both. Thecomputing resources comprising the cloud 150 may be centralized in asingle location, distributed throughout multiple locations, or acombination of both. A cloud computing system may divide computing tasksamongst multiple racks, blades, processors, cores, controllers, nodes orother computational units in accordance with a particular cloud systemarchitecture or programming. Similarly, a cloud computing system maystore instructions and computational information in a centralized memoryor storage, or may distribute such information amongst multiple storageor memory components. The cloud system may store multiple copies ofinstructions and computational information in redundant storage units,such as a RAID array.

The central processing unit 140 may be an example of a cloud computingsystem, or cloud-connected computing system. In embodiments, the centralprocessing unit 140 may be a server located within buildings in whichthe loads 111, 112, 113, 114, 115, and the IEDs 121, 122, 123, 124 areinstalled, or may be remotely-located cloud-based service. The centralprocessing unit 140 may include computing functional components similarto those of the IEDs 121, 122, 123, 124 is some embodiments, but maygenerally possess greater numbers and/or more powerful versions ofcomponents involved in data processing, such as processors, memory,storage, interconnection mechanisms, etc. The central processing unit140 can be configured to implement a variety of analysis techniques toidentify patterns in received measurement data from the IEDs 121, 122,123, 124, as discussed further below. The various analysis techniquesdiscussed herein further involve the execution of one or more softwarefunctions, algorithms, instructions, applications, and parameters, whichare stored on one or more sources of memory communicatively coupled tothe central processing unit 140. In certain embodiments, the terms“function”, “algorithm”, “instruction”, “application”, or “parameter”may also refer to a hierarchy of functions, algorithms, instructions,applications, or parameters, respectively, operating in parallel and/ortandem. A hierarchy may comprise a tree-based hierarchy, such a binarytree, a tree having one or more child nodes descending from each parentnode, or combinations thereof, wherein each node represents a specificfunction, algorithm, instruction, application, or parameter.

In embodiments, since the central processing unit 140 is connected tothe cloud 150, it may access additional cloud-connected devices ordatabases 160 via the cloud 150. For example, the central processingunit 140 may access the Internet and receive information such as weatherdata, utility pricing data, or other data that may be useful inanalyzing the measurement data received from the IEDs 121, 122, 123,124. In embodiments, the cloud-connected devices or databases 160 maycorrespond to a device or database associated with one or more externaldata sources. Additionally, in embodiments, the cloud-connected devicesor databases 160 may correspond to a user device from which a user mayprovide user input data. A user may view information about the IEDs 121,122, 123, 124 (e.g., IED makes, models, types, etc.) and data collectedby the IEDs 121, 122, 123, 124 (e.g., energy usage statistics) using theuser device. Additionally, in embodiments the user may configure theIEDs 121, 122, 123, 124 using the user device.

In embodiments, by leveraging the cloud-connectivity and enhancedcomputing resources of the central processing unit 140 relative to theIEDs 121, 122, 123, 124, sophisticated analysis can be performed on dataretrieved from one or more IEDs 121, 122, 123, 124, as well as on theadditional sources of data discussed above, when appropriate. Thisanalysis can be used to dynamically control one or more parameters,processes, conditions or equipment (e.g., loads) associated with theelectrical system.

In embodiments, the parameters, processes, conditions or equipment aredynamically controlled by a control system associated with theelectrical system. In embodiments, the control system may correspond toor include one or more of the IEDs 121, 122, 123, 124 in the electricalsystem, central processing unit 140 and/or other devices within orexternal to the electrical system.

Referring to FIG. 1B, an example IED 200 that may be suitable for use inthe electrical system shown in FIG. 1A, for example, includes acontroller 210, a memory device 215, storage 225, and an interface 230.The IED 200 also includes an input-output (I/O) port 235, a sensor 240,a communication module 245, and an interconnection mechanism 220 forcommunicatively coupling two or more IED components 210-245.

The memory device 215 may include volatile memory, such as DRAM or SRAM,for example. The memory device 215 may store programs and data collectedduring operation of the IED 200. For example, in embodiments in whichthe IED 200 is configured to monitor or measure one or more electricalparameters associated with one or more loads (e.g., 111, shown in FIG.1A) in an electrical system, the memory device 215 may store themonitored electrical parameters.

The storage system 225 may include a computer readable and writeablenonvolatile recording medium, such as a disk or flash memory, in whichsignals are stored that define a program to be executed by thecontroller 210 or information to be processed by the program. Thecontroller 210 may control transfer of data between the storage system225 and the memory device 215 in accordance with known computing anddata transfer mechanisms. In embodiments, the electrical parametersmonitored or measured by the IED 200 may be stored in the storage system225.

The I/O port 235 can be used to couple loads (e.g., 111, shown in FIG.1A) to the IED 200, and the sensor 240 can be used to monitor or measurethe electrical parameters associated with the loads. The I/O port 235can also be used to coupled external devices, such as sensor devices(e.g., temperature and/or motion sensor devices) and/or user inputdevices (e.g., local or remote computing devices) (not shown), to theIED 200. The I/O port 235 may further be coupled to one or more userinput/output mechanisms, such as buttons, displays, acoustic devices,etc., to provide alerts (e.g., to display a visual alert, such as textand/or a steady or flashing light, or to provide an audio alert, such asa beep or prolonged sound) and/or to allow user interaction with the IED200.

The communication module 245 may be configured to couple the IED 200 toone or more external communication networks or devices. These networksmay be private networks within a building in which the IED 200 isinstalled, or public networks, such as the Internet. In embodiments, thecommunication module 245 may also be configured to couple the IED 200 toa cloud-connected hub (e.g., 130, shown in FIG. 1A), or to acloud-connected central processing unit (e.g., 140, shown in FIG. 1A),associated with an electrical system including IED 200.

The IED controller 210 may include one or more processors that areconfigured to perform specified function(s) of the IED 200. Theprocessor(s) can be a commercially available processor, such as thewell-known Pentium™, Core™, or Atom™ class processors available from theIntel Corporation. Many other processors are available, includingprogrammable logic controllers. The IED controller 210 can execute anoperating system to define a computing platform on which application(s)associated with the IED 200 can run.

In embodiments, the electrical parameters monitored or measured by theIED 200 may be received at an input of the controller 210 as IED inputdata, and the controller 210 may process the measured electricalparameters to generate IED output data or signals at an output thereof.In embodiments, the IED output data or signals may correspond to anoutput of the IED 200. The IED output data or signals may be provided atI/O port(s) 235, for example. In embodiments, the IED output data orsignals may be received by a cloud-connected central processing unit,for example, for further processing (e.g., to identify power qualityevents, as briefly discussed above), and/or by equipment (e.g., loads)to which the IED is coupled (e.g., for controlling one or moreparameters associated with the equipment, as will be discussed furtherbelow). In one example, the IED 200 may include an interface 230 fordisplaying visualizations indicative of the IED output data or signals.The interface 230 may correspond to a graphical user interface (GUI) inembodiments, and the visualizations may include tolerance curvescharacterizing a tolerance level of the equipment to which the IED 200is coupled, as will be described further below.

Components of the IED 200 may be coupled together by the interconnectionmechanism 220, which may include one or more busses, wiring, or otherelectrical connection apparatus. The interconnection mechanism 220 mayenable communications (e.g., data, instructions, etc.) to be exchangedbetween system components of the IED 200.

It is understood that IED 200 is but one of many potentialconfigurations of IEDs in accordance with various aspects of thedisclosure. For example, IEDs in accordance with embodiments of thedisclosure may include more (or fewer) components than IED 200.Additionally, in embodiments one or more components of IED 200 may becombined. For example, in embodiments memory 215 and storage 225 may becombined.

Returning now to FIG. 1A, in order to accurately describe aperiodicevents such as voltage sags in an electrical system (such as theelectric system shown in FIG. 1A), it is important to measure theenergy-related signals (e.g., voltage signals) associated with theevent. Two attributes often used to characterize voltage sags andtransients are magnitude (deviation from the norm) and duration (lengthin time) of the event. Both parameters are instrumental in defining, andthus, mitigating these types of power quality issues. Scatter plots ofan event's magnitude (y-axis) versus its corresponding duration (x-axis)are typically shown in a single graph called a “Magnitude-Duration”plot, “Power Tolerance Curve”, or as referred to herein, a ToleranceCurve.

FIG. 2 illustrates a well-known Magnitude-Duration plot 250: theInformation Technology Industry (ITI) Curve (sometimes referred to as anITIC or CBEMA Curve) 260. The ITIC Curve 260 shows “an AC input voltageenvelope which typically can be tolerated (no interruption in function)by most Information Technology Equipment (ITE),” and is “applicable to120V nominal voltages obtained from 120V, 208Y/120V, and 120/240V 60Hertz systems.” The “Prohibited Region” in the graph includes any surgeor swell which exceeds the upper limit of the envelope. Events occurringin this region may result in damage to the ITE. The “No Damage Region”includes sags or interruptions (i.e., below the lower limit of theenvelope) that are not expected to damage the ITE. Additionally, the “NoInterruption in Function Region” describes the area between the bluelines where sags, swells, interruptions and transients can normally betolerated by most ITE.

As is known, constraints of the ITIC Curve 260 include:

-   -   1. It is a static/fixed envelope/curve,    -   2. It is proposed for IT,    -   3. It is intended for 120V 60 Hz electrical systems,    -   4. It is a standardized/generic graph describing what “normally”        should be expected,    -   5. It inherently provides no information regarding the        consequences of an event,    -   6. It is solely a voltage-based graph, and does not consider any        other electrical parameter(s), and    -   7. It is presented on a semi-log graph for multiplicative        efficiency.

It is understood that prior art tolerance curves such as the ITIC/CBEMA,SEMI Curve, or other manually configured curves are often nothing morethan suggestions for specific applications. They do not indicate how aspecific system or piece of equipment, apparatus, load, or controlsassociated with the equipment, apparatus, or load will actually respondto a sag/swell event, what the event's impact will be the electricalsystem, or how and where to economically mitigate the issues.Furthermore, zones (sub-systems) within the electrical system are alltreated the same, even though the majority of IEDs monitor multipleloads. A good analogy is a road atlas: while the atlas illustrates thelocation of the road, it does not indicate the location of road hazards,expected gas mileage, condition of the vehicle, construction, and soforth. A better approach is required to improve managing voltage sagsand swells in electrical systems.

With the foregoing in mind, the ability to provide customized tolerancecurves allows an energy consumer (and the systems and methods disclosedherein) to better manage their system through simplified investmentdecisions, reduced CAPEX and OPEX costs, identified and characterizedissues and opportunities, improved event ride-though, and ultimately,higher productivity and profitability.

A few example factors to be considered when leveraging the benefits ofproviding dynamic tolerance curves for energy consumers include:

-   -   1. No two customers are exactly alike and no two metering points        are identical. A dynamic tolerance curve is uniquely customized        to the point at which the metering data is collected on a        specific electrical system.    -   2. As events occur and are captured, a dynamic tolerance curve        is continuously updated according the unique responses of the        electrical system.    -   3. A dynamic tolerance curve can be applied to any type of        electrical system/any type of customer; it is not limited to ITE        systems.    -   4. A dynamic tolerance curve can also be used for substantially        any voltage level; it is not limited to 120-volt systems.    -   5. A dynamic tolerance curve does not have fixed scaling; it can        (and may need to) auto-scale.    -   6. It is possible to automatically aggregate dynamic tolerance        curves from discrete devices into a single dynamic system        tolerance curve.

With the foregoing in mind, there are a plurality of new potentialfeatures according to this disclosure that can produce numerous benefitsfor energy consumers. In embodiments, the goal of these features is tosimplify a generally complex topic into actionable opportunities forenergy consumers. Example features according to this disclosure are setforth below for consideration.

I. Dynamic Tolerance Curves

This embodiment of the disclosure comprises automatically adjusting asag/swell tolerance curve based on load impact as measured by a discreteIED. In this embodiment, “load impact” is determined by evaluating apre-event load against a post-event load (i.e., the load after theevent's onset). The difference between the pre-event and post eventloads (i.e., kW, current, energy, etc.) is used to quantify the event'simpact. The measure of “impact” may be calculated as a percent value,absolute value, normalized value, or other value useful to the energyconsumer. Further evaluations may include changes in voltage, current,power factor, total harmonic distortion (THD) levels, discrete harmoniccomponent levels, total demand distortion (TDD), imbalance, or any otherelectrical parameter/characteristic that can provide an indication ofthe type (load or source), magnitude, and location of change within theelectrical system. The source of data may originate from logged data,waveform data, direct MODBUS reads, or any other means.

FIG. 3 illustrates a typical tolerance curve (e.g., ITIC curve), whichis used as a baseline (also shown in FIG. 2). It should be noted that inembodiments substantially any known uniquely described tolerance curve(e.g., SEMI F47, ANSI, CBEMA, other custom curve) may be used as thebaseline tolerance curve because an intent of this embodiment of thedisclosure is to dynamically customize (i.e., change, update, revise,etc.) the tolerance curve so that it reflects the unique electricalvoltage event tolerance characteristics at the IED's point ofinstallation. As more events are captured and quantified by the IED, theaccuracy and characterization of the dynamic voltage tolerance curve mayimprove at that IED's point of installation. FIG. 3 is also shown as asemi-logarithmic graph; however, the dynamic tolerance curve may bescaled in any practical format for both analyses and/or viewingpurposes.

FIG. 4 illustrates an example voltage sag event (50% of nominal, 3milliseconds duration) on a standard/baseline tolerance curve thatresults in the loss of 20% of the load as determined by the IED. Theshaded area in FIG. 5 illustrates the difference between the baselinetolerance curve (e.g., as shown in FIG. 3) and the actual tolerance ofthe downstream metered load(s) due to the particular sensitivity at thislocation in the electrical system to this degree (magnitude andduration) of voltage sag. FIG. 6 illustrates an example automaticallycustomized and updated tolerance curve built from the event data pointand determined for the point where the IED is installed on theelectrical system. In embodiments, it assumes that anysag/swell/transient event with more severe characteristics (i.e., deepervoltage sag, greater voltage swells, larger transient, longer duration,etc.) will impact the load at least as severely as the event presentlybeing considered.

FIG. 7 illustrates a multitude of voltage sags/swells/transients on astandard/baseline tolerance curve. Some events are indicated asimpactful and some are indicated as not impactful, based on one or morechanging parameters at the moment of the event. FIG. 8 illustrates anautomatically customized and updated tolerance curve for the multitudeof impactful and non-impactful voltage sags/swells/transients asdetermined by the measured data taken from the point where the IED isinstalled on the electrical system.

a. Three-Dimension (3-D) Dynamic Tolerance Curves with Load Impact (AlsoSometimes Referred to Herein as “Dynamic Tolerance-Impact Curves”)

Standard tolerance curves (e.g., ITIC Curve, SEMI Curve, etc.) aredescribed in two-dimensional graphs with percent of nominal voltage onthe y-axis and duration (e.g., cycles, second, milliseconds, etc.) onthe x-axis, for example, as shown in FIG. 7. While the y-axis ispresented in units of percent of nominal voltage, it is understood thatthe y-axis units may also be in absolute units (e.g., real values suchas voltage), or substantially any other descriptor of the y-axisparameter's magnitude. Additionally, while the x-axis is logarithmic inFIG. 7, for example, it is understood that the x-axis does not have tobe logarithmic (for example, it can be linear as well). These 2-Dstandard tolerance curve graphs provide only a limited description of anevent's characteristics (magnitude and duration); they don't provideinformation related to an event's impact on the load(s). While theenergy consumer knows an event occurred, they cannot tell whether (andif so, to what degree) an event impacted their electrical system (andlikely, their operation).

Adding a third dimension to the tolerance curve graph allows the energyconsumer to visually identify the characterization of their system'ssag/swell/transient tolerance (at the metering point) related tomagnitude, duration, and a third parameter such as load impact. Again,load impact is determined by analyzing changes in the load (or otherelectrical parameter) before and after an event using logged data,waveform data, direct MODBUS read data, other data, or any combinationthereof.

Three-dimensional (3-D) tolerance curves in accordance with embodimentsof the disclosure may be adapted and/or oriented to any axis,perspective, scale, numerically ascending/descending, alphabetized,color, size, shape, electrical parameter, event characteristic, and soforth to usefully describe an event or events to the energy consumer.For example, FIG. 9 illustrates an exemplary orthographic perspective ofa tolerance-impact curve incorporating three parameters: 1) percent ofnominal voltage on the y-axis, 2) duration in cycles and seconds on thex-axis, and 3) percent load impacted on the z-axis. While the y-axis ispresented in units of percent of nominal voltage in the illustratedembodiment, it is understood that the y-axis units may also be inabsolute units (e.g., real values such as voltage), or substantially anyother descriptor of the y-axis parameter's magnitude. Additionally,while the x-axis is logarithmic in the illustrated embodiment, it isunderstood that the x-axis does not have to be logarithmic (for example,it can be linear as well). FIG. 10 illustrates an exemplary single-pointperspective 3-D view of the same tolerance-impact curve shown in FIG. 9,and incorporates the same respective parameters for the three axes. Italso attempts to integrate color shading to help illustrate the severityof the impact due to specific magnitude and duration events (least toworst; yellow to red, in the illustrated embodiment). FIG. 11 attemptsto illustrate an exemplary single-point perspective 3-D view of atolerance-impact curve incorporating magnitude, duration, percent loadimpact, shading, and event shape (to provide more event characteristicsin a single graph). Again, the load impact may be as a relativepercentage of the total load before the event (as shown in the graph),as a real value (e.g., kW, Amps, etc.), ascending or descending invalue, or any other manipulation of these or any other electricalparameters.

b. Three-Dimension (3-D) Dynamic Tolerance-Recovery Time Curves

Building on the previous section discussing load impact, in embodimentsit is also possible to use tolerance-impact curves to more directlyquantify the effect of a voltage sag/swell/transient event on an energyconsumer's operation. The time to recover from an event may directlyaffect the overall cost of a voltage event.

For the purpose of this disclosure, “recovery time” is defined as theperiod of time required to return the electrical system parameters backto (or approximately back to) their original state prior to the eventthat prompted their initial perturbation. In embodiments, recovery timeand load impact are independent variables; neither is dependent on theother. For example, a voltage event may impact a small percentage ofload, yet the recovery time may be considerable. Conversely, therecovery time from an extremely impactful event could be relativelyshort. Just as the impact of an event is dependent on a number offactors (some examples of which are set forth in the summary section ofthis disclosure), so too is the recovery time. A few examples of factorsthat can heavily influence the duration of recovery time include:ability to quickly locate event source (if it's within the facility),extent of equipment damage, spare parts availability, personnelavailability, redundant systems, protection schemes, and so forth.

One example method for calculating the recovery time includes measuringthe elapsed time between the occurrence of a first impactful event andthe point when the load exceeds a predetermined threshold of thepre-event load. For example, a 500 kW pre-event load with a 90% recoverythreshold will indicate the recovery has occurred at 450 kW. If it takes26 minutes for the metered load to meet or exceed 450 kW (i.e., 90% ofthe pre-event load), then the recovery time is equal to 26 minutes. Therecovery threshold can be determined using a relative percentage of thepre-event load, an absolute value (kW), the recovery of the voltage orcurrent levels, an external or manual trigger, a recognized standardvalue, a subjective configuration, or by some other method using anelectrical or non-electrical parameter(s).

FIG. 12 illustrates an exemplary orthographic perspective of atolerance-recovery time curve incorporating three parameters: 1) percentof nominal voltage on the y-axis, 2) duration in cycles and seconds (oralternatively, milliseconds) on the x-axis, and 3) recovery time orperiod in days, hours, and/or minutes on the z-axis. While the y-axis ispresented in units of percent of nominal voltage in the illustratedembodiment, it is understood that the y-axis units may also be inabsolute units (e.g., real values such as voltage), or substantially anyother descriptor of the y-axis parameter's magnitude. Additionally,while the x-axis is logarithmic in the illustrated embodiment, it isunderstood that the x-axis does not have to be logarithmic (for example,it can be linear as well). In embodiments, the z-axis (recovery time)may be configured to substantially any fixed scale (or auto-scaled), maybe listed in ascending or descending order, and may use substantiallyany known temporal unit. FIG. 13 illustrates an exemplary single-pointperspective 3-D view of the same tolerance-recovery time curve shown inFIG. 12, and incorporates the same respective parameters for the threeaxes. FIG. 13 also integrates color shading to help illustrate theseverity of the recovery time due to specific magnitude and durationevents (least to worst; yellow to red in the illustrated embodiment).FIG. 14 illustrates an exemplary single-point perspective 3-D view of atolerance-recovery time curve incorporating magnitude, duration,recovery time, shading, and event shape (to provide more eventcharacteristics in a single graph).

c. 3-D Dynamic Tolerance-Economic Impact Curves

The 3-D curves discussed above may also be used to illustrate economicimpact (e.g., production losses, restart losses, product/materiallosses, equipment losses, third-party losses, total losses, etc.) as itrelates to voltage events. Obviously, configuration may betime-consuming; however, the relationship between recovery time and anyrelevant economic factor can easily be shown and understood usingdynamic tolerance-economic impact curves. The cost of downtime (CoD) maybe initially used to determine a given economic cost during the recoveryperiod (assuming the CoD value is reasonable). Some studies indicateeach minute of downtime costs energy consumers in the automotiveindustry more than $22K. By contrast, the similar studies indicate thathealthcare industry energy consumers lose more than $10K/minute ofdowntime. Over time, energy consumers (and the systems and methodsdisclosed herein) can quantify their typical recovery time costs(whether it's linear or non-linear), or they may have a study done todetermine this relationship at their facility or business. Determiningthe relationship between voltage events and economic factors will allowenergy consumers to make faster and better decisions regardingcapitalization expenditures and/or the retention of services.

For example, FIG. 15 illustrates the production losses with respect to a50% of nominal voltage sag event with a duration of 3 milliseconds.Assuming the recovery time was 8 hours (see, e.g., FIG. 13) andproduction losses are an average of $2.5K/hour, the total productionlosses will be $20K. If ride-through capabilities can help avoid anoperational disruption at a cost of $50K, the payback for investing involtage sag ride-though equipment is may only be about 2.5 voltageevents, for example. As mentioned at the beginning of this document,studies have shown the average industrial customer experiences about 66voltage sags each year so a decision to mitigate should bestraightforward in this case.

d. Upstream/Downstream Tolerance-Impact Curves

As has been stated and is widely known, electrical systems are sensitiveto voltage events in varying degrees. For some energy consumers, voltageevents may just be a nuisance (no significant impact); for other energyconsumers, any small voltage anomaly may be catastrophic. As previouslydiscussed, quantifying the impact of voltage events helps energyconsumers determine the severity, prevalence, and influence of theseevents on their operation. If voltage events impact the energyconsumer's operation, the next step is identifying the source of theproblem.

Metering algorithms and other associated methods may be used todetermine whether a voltage event's source is upstream or downstreamfrom a metering point (e.g., an IED's electrical point of installationin an electrical system). For example, FIG. 16 illustrates a simpleelectrical network with three metering points (M₁, M₂, and M₃). A fault(X) is shown to occur between M₁ and M₂. In embodiments, algorithms inM₁ may indicate the source of the fault to be downstream (↓) from itslocation, and algorithms in M₂ may indicate the source of the fault tobe upstream (↑) from its location. Additionally, in embodimentsalgorithms in M₃ may indicate the source of the fault to be upstream(↑). By evaluating the fault as a system event (i.e., using data fromall three IEDs), in embodiments it is possible to generally identify thelocation of the fault's source within the electrical network (i.e., withrespect to the metering points).

This embodiment evaluates the impact of a voltage event against theindicated location (upstream or downstream from the metering point)related to the voltage event's source. This is very useful becauseupstream voltage event sources often require different mitigativesolutions (and associated costs) than downstream voltage event sources.Furthermore, there will likely be different economic considerations(e.g., impact costs, mitigation costs, etc.) depending on where thevoltage event source is located within the electrical system. The largerthe impacted area, the more expensive the cost may be to mitigate theproblem. Upstream voltage events can potentially impact a larger portionof the electrical network than downstream voltage events, and therefore,may be more expensive to mitigate. Again, the cost to mitigate voltageevents will be determined on a case-by-case basis since each meteringpoint is unique.

In embodiments, the IEDs installed at the metering points are configuredto measure, protect, and/or control a load or loads. The IEDs aretypically installed upstream from the load(s) because current flow tothe load(s) may be a critical aspect in measuring, protecting and/orcontrolling the load(s). However, it is understood that the IEDs mayalso be installed downstream from the load(s).

Referring to FIG. 16A, another example electrical system includes aplurality of IEDs (IED1, IED2, IED3, IED4, IED5) and a plurality ofloads (L1, L2, L3, L4, L5). In embodiments, loads L1, L2 correspond to afirst load type, and loads L3, L4, L5 correspond to a second load type.The first load type may be the same as or similar to the second loadtype in some embodiments, or different from the second load type inother embodiments. Loads L1, L2 are positioned at a location that is“electrically” (or “conductively”) downstream relative to at least IEDsIED1, IED2, IED3 in the electrical system (i.e., IEDs IED1, IED2, IED3are upstream from loads L1, L2). Additionally, loads L3, L4, L5 arepositioned at a location that is “electrically” downstream relative toat least IEDs IED1, IED4, IED5 in the electrical system (i.e., IEDsIED1, IED4, IED5 are upstream from loads L3, L4, L5).

In the illustrated embodiment, a power quality event (or fault) X isshown occurring upstream relative to loads L1, L2. Up arrows indicate“upstream” and down arrows indicate “downstream” in the exampleembodiment shown. As illustrated, IEDs IED1, IED2 are shown pointingtowards the fault X. Additionally, IEDs IED3, IED4, IED5 are shownpointing upstream. In embodiments, this is because the path leading tothe fault X is upstream from IEDs IED3, IED4, IED5 respective locationin the electrical system, and downstream from IEDs IED1, IED2 respectivelocation in the electrical system. In embodiments, algorithms thatdetermine a direction of the fault X may be located (or stored) in theIEDs, on-site software, cloud-based systems, and/or gateways, etc., forexample.

FIG. 17 illustrates a 2-D graph voltage tolerance curve of voltageevents captured by an IED similar to FIG. 7 above; however, the upstreamand downstream voltage events are uniquely denoted andsuperimposed/overlaid together. FIG. 18 illustrates a 2-D voltagetolerance curve that shows only the upstream voltage events which aredisaggregated from the total set of voltage events shown in FIG. 17.Similarly, FIG. 19 illustrates a 2-D voltage tolerance curve showingonly the downstream voltage events as disaggregated from the total setof voltage events shown in FIG. 17. These graphs allow energy consumers(and the systems and methods disclosed herein) to distinguish theupstream events from the downstream events, thus, helping to provide abetter visually intuitive view for identifying the primary location ofvoltage event sources (and perhaps, their causes). Of course, additionalor alternative characteristics, parameters, filters, and/or otherrelated information (e.g., electrical data, time, metadata, etc.) may beused, displayed and/or plotted to further effectively and productivelyembellish the voltage tolerance curves.

For example, FIG. 20 illustrates an exemplary orthographic perspectiveof a tolerance-impact-source location curve incorporating fiveparameters: 1) percent of nominal voltage on the y-axis, 2) duration incycles and seconds on the x-axis, and 3) percent load impacted on thez-axis. While the y-axis is presented in units of percent of nominalvoltage in the illustrated embodiment, it is understood that the y-axisunits may also be in absolute units (e.g., real values such as voltage),or substantially any other descriptor of the y-axis parameter'smagnitude. Additionally, while the x-axis is logarithmic in theillustrated embodiment, it is understood that the x-axis does not haveto be logarithmic (for example, it can be linear as well). Additionaldimensions are also included in FIG. 20 such as the recovery time (sizeof data point) and whether a particular event was upstream or downstreamfrom the metering point (data point center is white or black,respectively). Moreover, the z-axis could be made to show the recoverytime while the size of the data point could be used to indicate thepercent load impacted. It is understood that many otherparameters/dimensions may be incorporated as makes sense and/or isuseful.

e. Mitigation of Sag/Swell/Transient Impact Using Dynamic ToleranceCurves

As noted above, electrical systems are typically sensitive to voltageevents in varying degrees. For some energy consumers, voltage events mayjust be a nuisance (no significant impact); for other energy consumers,any voltage event may be catastrophic. As previously discussed,quantifying the impact of voltage events helps energy consumersdetermine the severity, prevalence, and influence of these events ontheir operation. If voltage events have an impact the energy consumer'soperation, the next step should be identifying the problem so it can bereduced or eliminated altogether.

In embodiments, eliminating or reducing the impact of voltagesags/swells/transients (and momentary, temporary and instantaneousinterruptions) for the various embodiments discussed throughout thedisclosure, can be generally accomplished in three ways: 1) removing thesource of the voltage events, 2) reducing the number or severity ofvoltage events produced by the source, or 3) minimizing the effects ofthe voltage events on impacted equipment. In some embodiments, it issubstantially difficult to remove the source (or sources) of voltageevents because these same sources are usually an integral component orload within the facility's electrical infrastructure, process, and/oroperation. Additionally, the voltage event's source may be located onthe utility, and thus, hamper the ability to directly address a problem.If the voltage event's source is located inside the energy consumer'sfacility, it may be possible to minimize voltage events at the source byusing different techniques or technologies (e.g., “soft-start” motorsinstead of “across the line” motor starting). In some embodiments,removing or replacing the source (or sources) of voltage events maycost-prohibitive and require an extensive redesign of an electricalsystem or subsystem. It is also possible to “desensitize” equipmentagainst the effects of voltage events such as sags, swells, andtransients. As always, there are economic trade-offs when consideringthe best approach to reduce or eliminate voltage issues. FIG. 21 is agenerally recognized illustration showing the progression in cost tomitigate voltage events and other PQ-related issues, which tends toincrease as the solution moves closer to the source. A thorough economicevaluation may include both the initial and total life cycle costs for agiven solution. Furthermore, it may be very important to consider theresponse of any prospective solution to both internal and externalsources of system voltage perturbations.

As an example, motors are an important electrical apparatus used in mostprocesses. Standard (across the line) motor starts typically producevoltage sags due to the impedance between the source and motor and themotor's inrush current, which is typically 6-10 times the full-loadcurrent rating. Removing the motor from the process would most likely beimpractical; however, reducing the voltage sag or minimizing its effectson adjacent equipment may be viable alternatives. A few examplesolutions may include using different motor technologies such asvariable speed drives or to employ motor soft-start techniques tocontrol or limit the inrush current (and thus, reduce or eliminate thevoltage sag at start-up). Another example solution is to deploy one ormore of several mitigative devices or equipment to reduce the voltageevent's impact on sensitive equipment. Again, each electrical system isunique, so the cost to mitigate power quality disturbances may vary fromlocation to location, system to system, and customer to customer.

This embodiment includes evaluating the ride-through characteristics ofa multitude of mitigative devices against the dynamic tolerance-impactcurves provided by each capable IED. The output of the evaluation mayindicate the additional ride-through benefits of applying any particularmitigative device to any specific metering location. Moreover, acomparison of the economic, operational, and/or other benefits betweentwo or more ride-through technologies or techniques for a specificsystem or sub-system may also be provided. In embodiments, in order toperform the evaluation, a managed collection (or library) of mitigativedevices' ride-through characteristics may be assessed. The managedcollection (or library) of mitigative devices may include (but not belimited to) characteristics and/or capabilities such as type,technology, magnitude vs. duration behavior, load constraints, typicalapplications, purchase costs, installation costs, operational costs,availability, purchase sources, dimensions/form factors, brands, and soforth for each known variety. In embodiments, the characteristics andcapabilities described in the managed collection of mitigative deviceswill be considered (as required and as available) for application atevery (or substantially every) discretely metered point (or sub-system)where data is obtainable and assessible. One or more ride-throughcharacteristics curves (indicating magnitude vs. duration ride-throughcapabilities) for any or every mitigative device found in the managedcollection (library) may be superimposed/overlaid on the dynamictolerance curve for at least one or more discrete metering point(s).Alternatively, the evaluation may be provided through some other meansaccordingly. One or more characteristics and/or capabilities of themitigative device(s) may be included in the evaluation against thedynamic tolerance curve based on factors such as those listed andavailable in the managed collection (or library). In embodiments, thisevaluation may be alarm-driven, manually or automatically triggered,scheduled, or otherwise initiated.

The dynamic tolerance-impact curves provided by each capable IED for theelectrical system's hierarchy (or portions of its hierarchy) may beevaluated against the ride-through characteristics of one or moremitigative devices. In embodiments, it may be more feasible,cost-effective, or otherwise beneficial to provide ride-throughimprovements as part of a system, sub-system process, and/or discretelocation. Whereas it may be economical/practical/feasible to apply onetype of ride-though mitigative solution for one device or onesub-system/zone, it may be more economical/practical/feasible to providea different ride-through mitigative solution for another device orsubsystem/zone within the electrical system. In short, the mosteconomical/practical/feasible ride-through mitigative solution may beprovided for the entire (or portion of the) electrical system based onthe information available. In embodiments, other factors may beconsidered when determining ride-through improvements for one or morelocations within an electrical system; however, this applicationemphasizes the importance of leveraging discretely established dynamictolerance curves from one or more IEDs.

FIG. 22 illustrates the 2-D dynamic tolerance curve from FIG. 5. Again,this example shows a tolerance curve that has been customized andupdated based on a single 50% voltage sag lasting 3 milliseconds andhaving a 20% load impact. An evaluation may be performed to ascertainthe most economic/practical/feasible approach in order to improve theride-through performance for this particular location in the electricalsystem. The managed collection (library) of mitigative devices may beassessed against suitable options and viable solutions. FIG. 23 showsthe ride-through characteristics (magnitude vs. duration) of SagFighter®by Schneider Electric, which claims to meet SEMI F47,superimposed/overlaid on top of the updated dynamic tolerance curve.FIG. 24 provides the energy consumer with a graphical indication ofSagFighter's ride-through benefits at this particular location in theelectrical system (as indicated by the shaded area in FIG. 24, forexample). Of course, the final mitigation device recommendation providedto the energy consumer may be dependent on more than the ride-throughcharacteristic of the mitigative device (e.g.economical/practical/feasible/etc.). Additionally, this approach may beprovided to multiple metered points across the electrical system orsubsystems.

f. Determining Opportunity Costs for Ride-Through Mitigative SolutionsUsing Dynamic Tolerance Curves

As is known, opportunity cost refers to a benefit or gain that couldhave achieved, but was forgone in lieu of taking an alternative courseof action. For example, a facility manager with a fixed budget may beable to invest funds to expand the facility OR to improve thereliability of the existing facility. The opportunity cost would bedetermined based on the economic benefit of the choice not taken by thefacility manager.

In this embodiment of the disclosure, the “opportunity cost” is expandedto include other benefits such as production losses, material losses,recovery time, load impact, equipment losses, third-party losses, and/orany other loss that is quantifiable by some measure. Additionally, an“alternative course of action” may be the decision to take no action atall. A few benefits of taking no action include resource savings,monetary savings, time savings, reduced operational impact, deferral,and so forth. That is to say, decision-makers often consider thebenefits of taking no action greater than the benefits of takingspecific action(s).

The decision not to take an action is often based on the lack ofinformation related to a given problem. For example, if someone cannotquantify the benefits of taking a particular action, they are lesslikely to take that action (which may be the wrong decision).Conversely, if someone is able to quantify the benefits of taking aparticular action, they are more likely to make the right decision(whether to take action or not take action). Moreover, having qualityinformation available provides the tools to produce other economicassessments such as cost/benefit analyses and risk/reward ratios.

This embodiment of this disclosure may continuously (orsemi-continuously) evaluate the impact of voltage events(sags/swells/transients) against the ride-through tolerancecharacteristics of one or more mitigative devices, apparatuses and/orequipment. The evaluation may consider historical data to continuouslytrack voltage events, associated discrete and combined system impact(e.g., as a relative value, absolute value, demand, energy, or otherquantifiable energy-related characteristic), sub-system and/or systemperspective, hierarchical impact from two or more devices, zones,cross-zones, or combination thereof. Information taken from theevaluation may be used to provide feedback and metrics regarding theoperational repercussions that could have been avoided if one or moremitigative devices, apparatuses, and/or equipment would have beeninstalled at a location (or locations).

For example, FIG. 25 provides a 2-D graph that illustrates events (andany associated impacts) that could have been avoided (green circles) ifthe decision had been made to install SagFighter® prior to therespective voltage event. FIG. 26 illustrates a similar graph as shownin FIG. 25, but also includes the estimated recovery time that couldhave been avoided had mitigative solutions been implemented prior to thevoltage events. Metrics associated with these potentially avoided events(e.g., relative impact (%), absolute impact (W, kW, etc.), recovery timeper event, accumulated recovery time, downtime, losses, otherquantifiable parameters, etc.) may also be provided to an energyconsumer to help justify investments to resolve voltage sag issues. Theenergy consumer (or systems and methods of the disclosure herein) couldalso choose what level of mitigation would be justifiable by comparingdiffering mitigation techniques to the historical tolerance curve data(i.e., the point of diminishing region of interest (ROI)). Metrics maybe listed per event or accumulated, provided in a table or graphed,analyzed as a discrete point or from two or more devices (i.e., a systemlevel perspective), or otherwise manipulated to indicate and/or quantifythe impact and/or opportunity cost for not installing voltage eventmitigation. The same information could be displayed a 3-D orthographicperspective of a tolerance-impact curve incorporating at least threeparameters such as: 1) percent of nominal voltage on the y-axis, 2)duration in cycles and seconds on the x-axis, and 3) percent loadimpacted (or recovery time in days, hours or minutes) on the z-axis.While the y-axis is presented in units of percent of nominal voltage inthe illustrated embodiment, it is understood that the y-axis units mayalso be in absolute units (e.g., real values such as voltage), orsubstantially any other descriptor of the y-axis parameter's magnitude.Additionally, while the x-axis is logarithmic in the illustratedembodiment, it is understood that the x-axis does not have to belogarithmic (for example, it can be linear as well). Other parameters,characteristics, metadata, and/or mitigative apparatus may similarly beincorporated into a graph and/or table.

g. Verifying the Effectiveness of Mitigation Techniques Using DynamicTolerance Curves

Re-evaluating or reassessing the decision to invest in a facility'sinfrastructure is often overlooked, presumed, or merely based onspeculation and supposition. In most cases the benefits of installingmitigative technologies are just assumed, but never quantified.Measurement and Verification (M&V) processes focus on quantifying energysavings and conservation; however, steps to improve reliability andpower quality are not considered.

Embodiments of this disclosure periodically or continuously provide thefollowing example benefits:

-   -   Allocate risks between contractors and their customer (e.g., for        performance contracts),    -   Accurately assess voltage events to quantify savings (in impact,        recovery time, uptime, losses, or other economic factors),    -   Reduce voltage quality uncertainties to reasonable levels,    -   Aid in monitoring equipment performance,    -   Identify additional monitoring and/or mitigation opportunities,    -   Reduce impact to targeted equipment, and    -   Improve operations and maintenance.

The dynamic voltage-impact tolerance curve provides a baseline ofvoltage events at each discretely metered point that captures impactedor potentially impacted processes, operations or facilities.Post-installation evaluations may be performed using data taken from theareas predicted to experience the benefits. In embodiments, thesepost-installation evaluations compare “before vs. after” to quantify thereal benefits of installing the mitigative equipment. Determinedquantities may include reduced event impact, recovery time, operationalcosts, maintenance costs, or any other operational or economic variable.An exemplary equation to determine the calculated savings due toinstalling mitigative equipment may be:

Savings=(baseline costs−reduced downtime costs)±Adjustments

where “reduced downtime costs” may include all or some combination ofthe following:

-   -   Reduced production losses,    -   Reduced restart losses,    -   Reduced product/material losses,    -   Reduced equipment losses,    -   Reduced 3^(rd) party costs, and    -   . . . and/or some other loss reduction.

Installation costs for the mitigative equipment may need to beconsidered, likely as an “adjustment,” in some embodiments.

FIG. 27 illustrates an example 2-D dynamic voltage tolerance curveaccording to the disclosure where the blue threshold lines (-) representthe ride-through baseline thresholds at a discretely metered point andthe pink line (-) represents the predicted improvement to the voltageevent ride-through thresholds by installing a certain type of mitigationequipment. The green circles in FIG. 27 indicate the voltage events (andconsequently, the recovery time) expected to be avoided by installingthe mitigation equipment. FIG. 28 illustrates an example 2-D dynamicvoltage tolerance curve according to the disclosure showing the actualvoltage events and recovery time avoided due to the installation of themitigation equipment. The orange line (-) illustrates the actualimprovement to the voltage ride-through thresholds by installing themitigation equipment. In this example, the mitigation equipmentsurpassed its expectations by avoiding three additional voltage eventsand 22 hours (42 actual events−20 predicted events) of additionalrecovery time.

Each electrical system is unique and will perform differently to somedegree. Embodiments of this disclosure use empirical data tocharacterize the actual performance of the mitigation equipment. Forexample, the actual thresholds for voltage ride-through (-) may performbetter than expected as shown in FIG. 28 because the downstream load onthe mitigation equipment was/is less than expected. This allows themitigation device to ride-through longer than anticipated. Conversely,exceeding the mitigation equipment's load rating would likely result ina worse-than-expected performance. As the mitigation equipment's loadcontinues to be increased beyond its rating, the voltage ride-throughthresholds (-) will approach the original voltage ride-through threshold(-) or possibly be even more severe.

A 3-D dynamic tolerance curve similar to the one shown in FIG. 15 may beproduced to better demonstrate the effect of mitigation on otherparameters such as load impact, recovery time, economic factors, etc. Inthis case, at least three dimensions would be used to characterize theelectrical system at the point of the IED's installation. A 3-Devaluation would provide an even better intuitive understanding of amitigation equipment's historical, present and/or future performance. Itwould also make the selection of mitigation equipment for futureapplications less complicated and more cost-effective.

Metrics associated with the expected (based on historical data) andactually avoided events (e.g., relative impact (%), absolute impact (W,kW, etc.), reduced losses, other quantifiable parameters, etc.) may beprovided to an energy consumer to help justify the original oradditional investments to resolve voltage sag issues. Metrics may belisted per event or accumulated, provided in a table or graphed,analyzed as a discrete point or from two or more devices (i.e., a systemlevel perspective), or otherwise manipulated to indicate and/or quantifythe benefits and/or costs per avoided minute of impact due to theinstallation of voltage event mitigation. The same information could bedisplayed as a 3-D orthographic perspective of a tolerance-impact curveincorporating at least three parameters such as: 1) percent of nominalvoltage on the y-axis, 2) duration in cycles and seconds on the x-axis,and 3) percent load impacted (or recovery time) on the z-axis. While they-axis is presented in units of percent of nominal voltage in theillustrated embodiment, it is understood that the y-axis units may alsobe in absolute units (e.g., real values such as voltage), orsubstantially any other descriptor of the y-axis parameter's magnitude.Additionally, while the x-axis is logarithmic in the illustratedembodiment, it is understood that the x-axis does not have to belogarithmic (for example, it can be linear as well). Other parameters,characteristics, metadata, and/or mitigative apparatus could similarlybe incorporated into a graph and/or table, for example.

II. Using Non-PQ IEDs to Help Quantify Voltage Event Impact

The ability to quantify the impact of a voltage event may be derivedfrom measured changes in energy, current, or power flows (i.e.,consumption). An IED may be used to provide these measurements. Themeasurements may be acquired in real-time (e.g., via direct MODBUSreads), historically (e.g., logged data), or by some other means.

Power monitoring systems often incorporate a diverse array of IEDs thatare installed throughout the energy consumer's electrical system. TheseIEDs may have different levels of capabilities and feature sets; somemore and some less. For example, energy consumers often install high-end(many/most capabilities) IEDs at the location where electrical energyenters their premises (M₁ in FIG. 29). This is done to acquire thebroadest understanding possible of the electrical signals' quality andquantity as received from the source (typically, the utility). Becausethe budget for metering is usually fixed and the energy consumer oftenwants to meter as broadly as possible across their electrical system,conventional wisdom stipulates using IEDs with progressively lowercapabilities as the installed meter points get closer to the loads (seeFIG. 29, for example). In short, the majority of facilities incorporatemany more low/mid-range IEDs than high-end IEDs.

“High-end” metering platforms (and some “mid-range” metering platforms)are more expensive and generally capable of capturing PQ phenomenaincluding high-speed voltage events. “Low-end” metering platforms areless expensive and generally have reduced processor bandwidth, samplerates, memory, and/or other capabilities as compared to high-end IEDs.The emphasis of low-end IEDs, including energy measurements taken inmost breakers, UPSs, VSDs, etc., is typically energy consumption orother energy-related functions, and perhaps some very basic PQ phenomena(e.g., steady-state quantities such as imbalance, overvoltage,undervoltage, etc.).

This feature leverages (i.e., interrelates, correlates, aligns, etc.)one or more voltage event indicators, statistical derivations and/orother information from a high-end IED with one or more similar and/ordisparate measured parameters from a low-end IED with the goal ofquantifying the impact, recovery time, or other event characteristic atthe low-end IED. One exemplary method to do this is by using the voltageevent timestamp (indicator of the moment a voltage event occurs) fromthe high-end IED as a reference point for evaluating a measurableparameter corresponding with the same timestamp at a low-end that doesnot inherently have the capability to capture voltage events. Dataevaluated at both the high-end, mid-range, and low-end IEDs may include(but not be limited to) the event magnitude, duration, phase or linevalues, energy, power, current, sequential components, imbalance,timestamp, pre/during/post-event changes, any other measured orcalculated electrical parameter, metadata, meter characteristics, and soforth. Again, the measurements may be acquired in real-time (e.g., viadirect MODBUS reads), historically (e.g., logged data), or by some othermeans.

Another example way to leverage non-PQ IEDs is to extend the use ofevent alarms (including voltage events) derived from high-end IEDs. Forexample, when a high-end IED detects a voltage event, coincident datafrom low-end IEDs is analyzed to ascertain the impact, recovery time, orother event characteristic and/or parameter. If analysis of data fromthe low-end IED indicates some level of impact did occur, a voltageevent alarm, impact alarm, and/or other alarm type may be generated bythe system performing the analysis of the coincident data. The alarminformation may include any relevant parameter and/or information asmeasured by the low-end IED, high-end IED, metadata, metercharacteristics, load impact, recovery time, which one or more high-endIEDs triggered the low-end IED alarm, and so forth.

FIGS. 29 and 30 illustrate a relatively simple example of thisembodiment of the disclosure. At time to, a high-end IED installed arespective metering point or location M₁ indicates the beginning of avoltage event. The pre-event load is measured and the recovery timeclock begins for the IED installed at the metering location M₁. Otherrelevant data, metrics and/or statistically derived information may alsobe measured or calculated as required. Simultaneously, the software(on-site and/or cloud-based) and/or hardware managing the meteringsystem evaluates the other connected IEDs to determine whether any otherIED installed at another respective metering point or location (e.g.,M₂, M₃, M₄, M₅, M₆, M₇, M₈, M₉, M₁₀) concurrently experienced animpactful event. In this example, the IED installed at metering locationM₇ is found to have experienced a coincident impactful event (the otherdevices are ignored in this example for the sake of simplicity). Thepre-event load is determined from M₇ and the recovery time clock beginsfor M₇ using the voltage event's timestamp as a reference. With the IEDsinstalled at metering locations M₁ and M₇ identified as impacted by thevoltage event, the impact is quantified based on pre/during/post-eventelectrical parameters (e.g., power, current, energy, voltage, etc.) withto derived from the IED installed at metering location M₁ and used as areference point for both devices M₁ and M₇. The IED installed atmetering location M₇ is located downstream from the IED installed atmetering location M₁ and experiences a more significant relative impact(i.e., bigger percentage of its pre-event load) due to system impedanceand uniquely affected loads. The recovery time counters for the IEDsinstalled at metering locations M₁ and M₇ are independent of each other,as will be the case for all IEDs. In this example, the IED installed atmetering location M₇ experiences approximately the same recovery time asthe IED installed at metering location M₁ (i.e., t_(M1r)≈t_(M7r));however, that may not always be the case since recovery time may beunique at each metered location.

In embodiments, virtual metering may also be used to identify an impactof a voltage event on unmetered loads. For example, there are twoelectrical paths downstream from the IED installed at metering locationM₁ in FIG. 30A. The electrical path on the right is metered through aphysical IED (e.g., installed at metering location M₂); however, theelectrical path on the left is not directly metered by a physical IED.If the load data measured by the IEDs installed at metering locations M₁and M₂ are measured synchronously or pseudo-synchronously, it ispossible to determine (within the accuracy and synchronizationconstraints of the IEDs installed at metering locations M₁ and M₂) theload flowing through the unmetered path, V₁, by the following equation:V₁=M₁−M₂. V₁ represents a location of a “virtual meter” or a “virtualIED” in the electrical system, and it signifies the difference betweenthe IEDs installed metering locations M₁ and M₂ for any synchronous (orpseudo-synchronous) load measurement.

For this example, consider a fault that occurs downstream from the IEDinstalled at metering location M₁ and upstream from the virtual meterlocated at metering location V₁ in FIG. 30A. Using the concept ofvirtual metering as described above, a load change is determined to haveoccurred in the unmetered path. Because the load data through theunmetered path may be derived from the IEDs installed at meteringlocations M₁ and M₂, it is possible to calculate the load impact to theunmetered path due to the fault. In this example, other importantparameters related to this embodiment of the disclosure may also bederived from virtual meters including recovery time, economic impact,and so forth.

In one embodiment, the data sample rate (e.g., power, current, energy,voltage, or other electrical parameters) for IEDs installed at meteringlocations M₁, M₇, and/or any other IEDs may be dependently orindependently increased as required after a voltage event has beenindicated in order to provide more accurate results (e.g., recoverytime). Data may be shown in a tabular format, graphically in 2-D or 3-D,color coded, as timelines from discrete IEDs, zonally, hierarchically,or as a system (aggregated) view, linearly or logarithmically, or in anyother structure or method considered relevant and/or useful. The outputof this embodiment may be via report, text, email, audibly,screen/display, or by some other interactive means.

Referring to FIGS. 30B-30I, several example figures are provided tofurther illustrate the concept of virtual metering in accordance withembodiments of this disclosure. As discussed above, an electrical systemtypically includes one or more metering points or locations. As alsodiscussed above, one or more IEDs (or other meters, such as virtualmeters) may be installed or located (temporarily or permanently) at themetering locations, for example, to measure, protect, and/or control aload or loads in the electrical system.

Referring to FIG. 30B, an example electrical system including aplurality of metering locations (here, M₁, M₂, M₃) is shown. In theillustrated embodiment, at least one first IED is installed at the firstmetering location M₁, at least one second IED is installed at the secondmetering location M₂, and at least one third IED is installed at thethird metering location M₃. The at least one first IED is a so-called“parent device(s),” and the at least one second IED and the at least onethird IED are so-called “child devices.” In the example embodimentshown, the at least one second IED and the at least one third IED arechildren of the at least one first IED (and, thus siblings with eachother), for example, due to the at least one second IED and the at leastone third IED both being installed at respective metering locations M₂,M₃ in the electrical system that “branch” from a common point (here,connection 1) associated with the metering location M₁ at which the atleast one first IED is installed. Connection 1 is the physical point inthe electrical system where the energy flow (as measured at M₁ by the atleast one first IED) diverges to provide energy to the left and rightelectrical system branches (as measured at M₂ and M₃ by the at least onesecond IED and the at least one third IED, respectively).

The electrical system shown in FIG. 30B is an example of a “completelymetered” system, where all branch circuits are monitored by a physicalIED (here, the at least one first IED, the at least one second IED, andthe at least one third IED). In accordance with various aspects of thisdisclosure, dynamic tolerance curves can be independently developed foreach discrete metered location (M₁, M₂, M₃) without any dependence onexternal input(s) from other IEDs. For example, electrical measurementdata from energy-related signals captured by the at least one first IEDinstalled at the first metering location M₁ may be used to generate aunique dynamic tolerance curve for the metering location M₁ (e.g., asshown in FIG. 30C) without any input (or data) from the at least onesecond IED or the at least one third IED. Additionally, electricalmeasurement data from energy-related signals captured by the at leastone second IED installed at the second metering location M₂ may be usedto generate a unique dynamic tolerance curve for the metering locationM₂ (e.g., as shown in FIG. 30D) without any input (or data) from the atleast one first IED or the at least one third IED. Further, electricalmeasurement data from energy-related signals captured by the at leastone third IED installed at the third metering location M₃ may be used togenerate a unique dynamic tolerance curve for the metering location M₃(e.g., as shown in FIG. 30E) without any input (or data) from the atleast one first IED or the at least one second IED.

Referring to FIG. 30F, in which like elements of FIG. 30B are shownhaving like reference designations, another example electrical system isshown. Similar to the electrical system shown in FIG. 30B, theelectrical system shown in FIG. 30F includes a plurality of meteringlocations (here, M₁, M₂, V₁). Also, similar to the electrical systemshown in FIG. 30B, the electrical system shown in FIG. 30F includes atleast one metering device installed or located at each of the meteringlocations (M₁, M₂). Here, however, unlike the electrical system shown inFIG. 30B, the electrical system shown in FIG. 30F includes a virtualmeter (V₁) accordance with embodiments of this disclosure.

In the illustrated embodiment, at least one first IED is installed at afirst “physical” metering location M₁, at least one second IED isinstalled at a second “physical” metering location M₂, and at least onevirtual meter is derived (or located) at a “virtual” (non-physical)metering location V₁. The at least one first IED is a so-called “parentdevice” and the at least one second IED and the at least one virtualmeter are so-called “child devices”. In the example embodiment shown,the at least one second IED and the at least one virtual meter arechildren of the at least one first IED (and, thus considered to besiblings with each other). In the illustrated embodiment, the at leastone second IED and the at least one virtual meter are installed andderived, respectively, at respective metering locations M₂, V₁ in theelectrical system that “branch” from a common point (here, connection 1)associated with the metering location M₁ at which the at least one firstIED is installed. Connection 1 is the physical point in the electricalsystem where the energy flow (as measured at M₁ by the at least onefirst IED) diverges to provide energy to the left and right branches (asmeasured at M₂ by the at least one second IED, and as calculated for V₁by the at least one virtual meter).

In accordance with embodiments of this disclosure, electricalmeasurement data associated with the virtual metering location V₁ may becreated/derived by calculating the difference between the synchronous(or pseudo-synchronous) data from the at least one first IED (here, aparent device) installed at the first metering location M₁ and the atleast one second IED (here, a child device) installed at the secondmetering location M₂. For example, electrical measurement dataassociated with the virtual metering location V₁ may be derived bycalculating the difference between electrical measurement data fromenergy-related signals captured by the at least one first IED andelectrical measurement data from energy-related signals captured by theat least one second IED, at a specific point in time (e.g., V₁=M₁−M₂,for synchronous or pseudo-synchronous data). It is understood thatvirtual meters (e.g., the at least one virtual meter located at virtualmetering location V₁) may include data from one or more unmetered branchcircuits, which are inherently aggregated into a single representativecircuit.

The electrical system shown in FIG. 30F is an example of a “partiallymetered” system, where only a subset of the total circuits is monitoredby physical IEDs (here, the at least one first IED and the at least onesecond IED). In accordance with various aspects of this disclosure,dynamic tolerance curves can be independently developed for eachphysically metered point (M₁, M₂) without any dependence on externalinput(s) from other IEDs. Additionally, in accordance with variousaspects of this disclosure, the dynamic tolerance curve for a virtuallymetered point (V₁) is derived using select synchronous (orpseudo-synchronous) and complementary data (e.g., power, energy,voltage, current, harmonics, etc.) from physical IEDs (here, the atleast one first IED, and the at least one second IED), and is dependent(sometimes, completely dependent) on these devices (here, the at leastone first IED, and the at least one second IED). For example, returningbriefly to FIGS. 30C-30E, the dynamic tolerance curve for virtualmetered point V₁ may be derived from the dynamic tolerance curve datafor physical metered points M₁, M₂ (e.g., as shown in FIGS. 30C and 30D,respectively). Because of this dependency, it is understood that issues(e.g., accuracy, missing data, non-synchronous data, etc.) with the atleast one first IED and the at least one second IED will be reflected inthe resulting virtual meter data in the illustrated embodiment. In theillustrated embodiment, the dynamic tolerance curve for virtual meteredpoint V₁ may be the same as (or similar to) the dynamic tolerance curveshown in FIG. 30E as an example.

Referring to FIG. 30G, a further example electrical system includes atleast one virtual meter located at a “virtual” metering location V₁, atleast one first IED installed at a first “physical” metering locationM₁, and at least one second IED installed at a second “physical”metering location M₂. The at least one virtual meter is a so-called“parent device” or “virtual parent device,” and the at least one firstIED and the at least one second meter are “child devices.” In theexample embodiment shown, the at least one first IED and the at leastone second IED are children of the at least one virtual meter (and, thusconsidered to be siblings with each other).

As illustrated, the at least one first IED and the at least one secondIED are both installed (or located) at respective metering locations M₁,M₂ in the electrical system that “branch” from a common point (here,connection 1) associated with the virtual metering location V₁ at whichthe at least one virtual meter is derived (or located). Connection 1 isthe physical point in the electrical system where the energy flow (ascalculated at V₁) diverges to provide energy to the left and rightbranches (as measured at M₁ and M₂ by the at least one first IED and theat least one second IED, respectively).

In accordance with embodiments of this disclosure, electricalmeasurement data associated with the first metering location V₁ iscreated/derived through a slightly different approach than describedabove in connection with FIG. 30F, for example. In particular, theelectrical measurement data associated with the first metering locationV₁ may be determined by calculating the summation of synchronous (orpseudo-synchronous) data from the at least one first child IED installedat metering location M₁ and the at least one second child IED deviceinstalled at metering location M₂ (e.g., V₁=M₁+M₂, for synchronous orpseudo-synchronous data).

The electrical system shown in FIG. 30G is an example of a “partiallymetered” system, where only a subset of the total circuits is monitoredby physical IEDs. In accordance with various aspects of this disclosure,dynamic tolerance curves can be independently developed for eachphysically metered point (M₁, M₂) without any dependence on externalinput(s) from other IEDs. Additionally, in accordance with variousaspects of this disclosure, the dynamic tolerance curve for the virtualparent meter (V₁) is derived using select complementary data (e.g.,power, energy, voltage, current harmonics, etc.) from physical IEDs (M₁,M₂), and is completely dependent on these devices (M₁, M₂). Because ofthis dependency, it is understood that any issue (e.g., accuracy,missing data, non-synchronous data, etc.) with meters M₁ and M₂ will bereflected in virtual parent device V₁.

Referring to FIG. 30H, another example electrical system includes atleast one first virtual meter located at a first “virtual” meteringlocation V₁, at least one first IED installed at a first “physical”metering location M₁, and at least one second virtual meter installed ata second “virtual” metering location V₂. The at least one virtual meteris a “parent device” or a “virtual parent device”, and the at least onefirst IED and the at least one second virtual meter are “child devices”.In the example embodiment shown, the at least one first IED and the atleast one second virtual meter are children of the at least one firstvirtual meter (and, thus considered to be siblings with each other).

As illustrated, the at least one first IED and the at least one secondvirtual meter are installed and derived, respectively, at respectivemetering locations M₁, V₂ in the electrical system that “branch” from acommon point (here, connection 1) associated with the first virtualmetering location V₁ at which the at least first one virtual meter islocated (or derived). Connection 1 is the physical point in theelectrical system where the energy flow (as calculated at V₁) divergesto provide energy to the left and right branches (as measured at M₁ bythe at least one first IED, and as calculated at V₂).

In accordance with some embodiments of this disclosure, the electricalsystem shown in FIG. 30H is mathematically and probabilisticallyindeterminate because there are too many unknown values from necessaryinputs. Assumptions may be made regarding the occurrence of powerquality events (e.g., voltage events) on the virtual devices (V₁, V₂);however, the impact of the power quality events may impossible (orextremely hard) to define in this case. As is appreciated fromdiscussions above and below, virtual metering data is derived from datataken from physical IEDs. In the embodiment shown in FIG. 30H, there aretoo few physical IEDs to derive the “virtual” data. FIG. 30H is shown toillustrate some constraints related to virtual IED derivations.

Referring to FIG. 30I, a further example electrical system includes atleast four virtual meters (or IEDs) located (or derived) at respective“virtual” metering locations (V₁, V₂, V₃, V₄) in the electrical system,and at least five IEDs installed at respective “physical” meteringlocations (M₁, M₂, M₃, M₄, M₅) in the electrical system. In particular,the electrical system includes at least one first “parent” virtual meterlocated at a first “virtual” metering location V₁, at least one first“child” IED installed at a first “physical” metering location M₁, and atleast one second “child” IED installed at a second “physical” meteringlocation M₂ (with the at least one first IED at metering location M₁ andthe at least one second IED at metering location M₂ being children ofthe at least one first virtual meter at metering location/position V₁).The electrical system also includes at least one third “child” IEDinstalled at a third “physical” metering location M₃ and at least onesecond “child” virtual meter located at a second “virtual” meteringlocation V₂ (with the at least one third IED at metering location M₃ andthe at least one second virtual meter at metering location V₂ beingchildren of the at least one first IED at metering location M₁).

The electrical system further includes at least one fourth “child” IEDinstalled at a fourth “physical” metering location M₄ and at least onethird “child” virtual meter located at a third “virtual” meteringlocation V₃ (with the at least one fourth IED at metering location M₄and the at least one third virtual meter at metering location V₃ beingchildren of the at least one second virtual meter at metering locationV₂). The electrical system also includes at least one fifth “child” IEDinstalled at a fifth “physical” metering location M₅ and at least onefourth “child” virtual meter located at a fourth “virtual” meteringlocation V₄ (with the at least one fifth IED at metering location M₅ andthe at least one fourth virtual meter at metering location V₄ beingchildren of the at least one third virtual meter at metering locationV₃). As illustrated, there are essentially five layers in the meteringhierarchy from the first virtual metering location V₁, to the fifth“physical” metering location M₅ and the fourth “virtual” meteringlocation V₄.

The electrical system shown in FIG. 30I illustrates a partially meteredsystem, where only a subset of the total circuits is monitored byphysical devices/IEDs. In accordance with various aspects of thisdisclosure, dynamic tolerance curves can be independently developed foreach physically metered location (M₁, M₂, M₃, M₄, M₅) without anydependence or interdependence on external input(s) from other IEDs. Thedynamic tolerance curves for the virtual metering locations V₁, V₂, V₃,V₄ may be derived from complementary and synchronous (orpseudo-synchronous) data (e.g., power, energy, voltage, current,harmonics, etc.) as measured by physical IEDs installed at thediscretely metered locations M₁, M₂, M₃, M₄, M₅. Additionally,electrical measurement data from energy-related signals captured by theat least one second IED installed at the second metering location M₂ maybe used to generate a dynamic tolerance curve for the metering locationM₂ without any input (or data) from the at least one first IED or the atleast one third IED.

In particular, the electrical measurement data associated with the firstvirtual metering location V₁ may be determined (and used to helpgenerate a dynamic tolerance curve for the first virtual meteringlocation V₁) by calculating the summation of synchronous (orpseudo-synchronous) data from the at least one first child IED installedat metering location M₁ and the at least one second child IED deviceinstalled at metering location M₂ (e.g., V₁=M₁+M₂, for synchronous orpseudo-synchronous data). Additionally, the electrical measurement dataassociated with the second metering location V₂ may be determined (andused to help generate a dynamic tolerance curve for the second virtualmetering location V₂) by calculating the difference between synchronous(or pseudo-synchronous) data from the at least one first child IEDinstalled at metering location M₁ and the at least one third child IEDdevice installed at metering location M₃ (e.g., V₂=M₁−M₃, forsynchronous or pseudo-synchronous data).

The electrical measurement data associated with the third virtualmetering location V₃ may be determined (and used to help generate adynamic tolerance curve for the third virtual metering location V₃) byfirst calculating the difference between synchronous (orpseudo-synchronous) data from the at least one first child IED installedat metering location M₁ and the at least one third child IED deviceinstalled at metering location M₃, and then calculating the differencebetween the first calculated difference and synchronous (orpseudo-synchronous) data from the at least one fourth child IEDinstalled at metering location M₄ (e.g., V₃=M₁−M₃−M₄, for synchronous orpseudo-synchronous data).

Additionally, the electrical measurement data associated with the fourthvirtual metering location V₄ may be determined (and used to helpgenerate a dynamic tolerance curve for the fourth virtual meteringlocation V₄) by first calculating the difference between synchronous (orpseudo-synchronous) data from the at least one first child IED installedat metering location M₁ and the at least one third child IED deviceinstalled at metering location M₃, and then calculating the differencebetween the synchronous (or pseudo-synchronous) data from the at leastone fourth child IED installed at metering location M₄ and the at leastone fifth child IED installed at metering location M₅. The differencebetween the first calculated difference and the calculated differencebetween the synchronous (or pseudo-synchronous) data from the at leastone fourth child IED installed at metering location M₄ and the at leastone fifth child IED installed at metering location M₅ may be used todetermine the electrical measurement data associated with the fourthvirtual metering location V₄ (e.g., V₄=M₁−M₃−M₄−M₅, for synchronous orpseudo-synchronous data).

As will be further appreciated from discussions below, using eventtriggers or alarms from one or more of the physical IEDs (M₁, M₂, M₃,M₄, M₅), it is possible to use pre-event and post-event data from thephysical IEDs to develop dynamic tolerance curves, determine eventimpacts, quantify recovery times, and assess other associated costs atthe virtual meters (and metering locations V₁, V₂, V₃, V₄). Again,validity of the derived information for the virtual meter (V₁, V₂, V₃,V₄) is dependent on the veracity, accuracy, synchronicity, andavailability of data from the physical IEDs (M₁, M₂, M₃, M₄, M₅). Inthis particular case, there are many interdependencies used to derivedata for the virtual meters (and metering locations V₁, V₂, V₃, V₄), soit is understood that some deficiency may be experienced for one or morederivations.

It is understood that the above-described examples for determining,deriving, and/or generating dynamic tolerance curves for virtual metersin an electrical system may also apply to aggregation of zones andsystems. In spirit of the concepts describing “operational impact,”“recovery time,” “recovery energy costs,” and so forth, it is understoodthat aggregation may only make sense when it is 1) directly useful tothe customer/energy consumer, 2) and/or useful to be leveraged foradditional customer and/or business-centered benefits (present orfuture). That is why the best approach to aggregation is typically tofocus on the worst-case scenario (i.e., event impact, event recoverytime, other associated event costs, etc.). If aggregation is performedand it does not reflect the customers experience in trying to resolvethe event in question, then it is difficult to achieve any usefulnessfrom the aggregation. In short, just because something is mathematicallyand/or statistically feasible does not necessarily make it useful.

III. Evaluating Load Impact and Recovery Time Using Hierarchy andDynamic Tolerance Curve Data

In embodiments, when a load impacting voltage event occurs, it isimportant for the energy consumer (or the systems and methods disclosedherein) to prioritize the “what, when, why, where, who, how/how much/howsoon, etc.” of the response. More specifically: 1) what happened, 2)when did it happen, 3) why did it happen, 4) where did it happen, 5)who's responsible, 6) how do I resolve the issue, 7) how much is itgoing to cost, and 8) how soon can I get it resolved. Embodimentsdescribed herein assist energy consumers with answering these questions.

Understanding and quantifying the impact of voltage (and/or other)events from a IED, zone, and/or system perspective is extremelyimportant for energy consumers to understand their electrical system andfacility's operation in its entirety, and to respond to electricalevents accordingly. Because each load has unique operatingcharacteristics, electrical characteristics and ratings, functions, andso forth, the impact of a voltage event may differ from one load to thenext. This can result in unpredictable behavior, even with comparableloads connected to the same electrical system and located adjacent toeach other. It is understood that some aspects of the embodimentsdescribed below may refer to or overlap with previously discussed ideaspresented herein.

System (or hierarchical) perspectives show how an electrical system ormetering system is interconnected. When a voltage event occurs, itsimpact is strongly influenced by the system impedance and sensitivity ofa given load. For example, FIG. 31 illustrates a relatively simplefully-metered electrical system experiencing a voltage event (e.g., dueto a fault). In general, the system impedance will dictate the magnitudeof the fault, protective devices will dictate the duration of the fault(clearing time), and location of the fault will be an important factorin the scope of the fault's impact to the electrical system. In FIG. 31,its possible (even likely) the shaded area will experience a significantvoltage sag followed by an interruption (due to the operation ofprotective device(s)). In embodiments, the duration of the event'simpact will be from the onset time of the fault until the system isagain operating normally (note: this example states a recovery time of 8hours). The unshaded area of the electrical system in FIG. 31 may alsoexperience a voltage event due to the fault; however, the recovery timefor the unshaded area will likely be briefer than the shaded area.

In embodiments, both the shaded and unshaded areas of the electricalsystem shown in FIG. 31 may be impacted by the fault; however, both mayexhibit different recovery time durations. If the processes served byboth the shaded and unshaded areas are critical to the facility'soperation, then the system recovery time will be equal to the greater ofthe two recovery times.

In embodiments, it is important to identify and prioritize IEDs, zones,and/or systems. Zones may be determined within the electrical systemhierarchy based on: protection schemes (e.g., each breaker protects azone, etc.), separately derived sources (e.g., transformers, generators,etc.), processes or sub-systems, load types, sub-billing groups ortenants, network communications schemes (e.g., IP addresses, etc.), orany other logical classification. Each zone is a subset of the meteringsystem's hierarchy, and each zone may be prioritized by type and eachzone may be assigned more than one priority if applicable (e.g., highpriority load type with low priority process). For example, if aprotective device also acts as a IED and is incorporated into themetering system, it and the devices below it could be considered a zone.If the protective devices are layered in a coordinated scheme, the zoneswould be similarly layered to correspond with the protective devices. InFIG. 32, another method to automatically determine zones involvesleveraging hierarchical context to evaluate voltage, current, and/orpower data (other parameters may also be used as necessary) to identifytransformer locations. FIG. 32 indicates three zones: utility source,transformer 1, and transformer 2. FIG. 33 is an exemplary illustrationof an energy consumer's custom zone configuration.

Once the zones are established, prioritizing each zone will help theenergy consumer better respond to voltage events (or any other event)and their impact. While there are techniques to automatically prioritizezones (e.g., largest to smallest load, load types, recovery times,etc.), the most prudent approach would be for the energy consumer torank the priorities of each zone. It is certainly feasible (andexpected) for two or more zones to have an equal ranking in priority.Once zone priorities are established, it is then possible to analyze theload impact and recovery time for voltage events from a zonalperspective. Again, all of this may be automated using the techniquesdescribed above for establishing zones, prioritizing based on thehistorical effects of voltage events within the electrical system, andproviding the energy consumer with analyses summaries based on theseclassifications.

Zones are also useful for identifying practical and economicalapproaches to mitigate voltage events (or other PQ issues). Becausemitigation solutions can range from system-wide to targeted schemes, itis beneficial to evaluate mitigation opportunities the same way. Asshown in FIG. 21 above, for example, mitigation solutions for voltageevents become more expensive as the proposed solution moves closer tothe electrical main switchgear.

In embodiments, evaluating zones to identify mitigation opportunities ofvoltage events can produce a more balanced, economical solution. Forexample, one zone may be more susceptible to voltage events (e.g.,perhaps due to a local motor starting) than another zone. It may bepossible to provide electrical service to sensitive loads from anotherzone. Alternatively, it may be prudent to move the cause of the voltageevents (e.g., the local motor) to another service point in another zone.

A further example benefit of evaluating zones is the ability toprioritize capital expenditure (CAPEX) investments for voltage eventmitigation based on the prioritization of each respective zone. Assumingthe zones have been properly prioritized/ranked, important metrics suchas percent load impacted (relative), total load impacted (absolute),worst case severity, recovery time, etc. may be aggregated over time toindicate the best solution and location for mitigative equipment. Usingaggregated zonal voltage tolerance data from IEDs within the zone mayprovide a “best-fit” solution for the entire zone or locate a targetedsolution for one or more loads within a zone.

IV. Alarm Management of IEDs Using Dynamic Tolerance Curves andAssociated Impact Data

As discussed above, each location within an electrical system/networkgenerally has unique voltage event tolerance characteristics.Dynamically (continuously) generating the distinct voltage eventtolerance characteristics for one or more metered points in theelectrical system provides many benefits including a betterunderstanding of an electrical system's behavior at the metered point,suitable and economical techniques for mitigating voltage anomalies,verification that installed mitigation equipment meets its designcriteria, leveraging non-PQ IEDs to help characterize voltage eventtolerances, and so forth.

Another example advantage of characterizing a IED point's voltage eventtolerance is to customize alarms at the IED's point of installation.Using dynamic voltage event characterization to manage alarms providesseveral benefits including ensuring 1) relevant events are captured, 2)excessive alarms are prevented (better “alarm validity”), 3) appropriatealarms are configured, and 4) important alarms are prioritized.

Existing approaches to alarm configuration and management often include:

-   -   Manual configuration by energy consumer based on standards,        recommendations, or guessing.    -   Some form of setpoint learning that necessitated a configuration        “learning period” to determine what was normal. Unfortunately,        if an event occurred during the learning period, it would be        considered normal behavior unless the energy consumer caught it        and omitted the data point.    -   “Capture Everything” approach that requires the energy consumer        to apply filters to distinguish which alarms are important and        which are not.

In short, the energy consumer (who may not be an expert) could berequired to actively discriminate which event alarms/thresholds areimportant, either before or after the event alarms are captured in a“live system.”

Currently, IED voltage event alarms have two important thresholds thatare typically configured: 1) magnitude, and 2) duration (sometimesreferred to as alarm hysteresis). Equipment/loads are designed tooperate at a given optimal voltage magnitude (i.e., rated voltage)bounded by an acceptable range of voltage magnitudes. Additionally, itmay be possible for a load to operate outside the acceptable voltagerange, but only for short periods of time (i.e., duration).

For example, a power supply may have a rated voltage magnitude of 120volts rms±10% (i.e., ±12 volts rms). Therefore, the power supplymanufacturer is specifying the power supply should not be operatedcontinuously outside the range of 108-132 volts rms. More precisely, themanufacturer is making no promises regarding the power supply'sperformance or susceptibility to damage outside their prescribed voltagerange. Less evident is how the power supply performs during momentary(or longer) voltage excursions/events outside the prescribed voltagerange. Power supplies may provide some voltage ride-though due to theirinherent ability to store energy. The length of voltage ride-throughdepends on a number of factors, primarily the amount/quantity of loadconnected to the power supply during the voltage excursion/event. Thegreater the load on the power supply, the shorter the power supply'sability to ride-though the voltage excursion/event. In summary, thissubstantiates the two parameters (voltage magnitude and duration duringthe voltage event), which also happen to be the same two parametersexemplified in basic voltage tolerance curves. It further validates loadas an additional parameter that may be considered where a voltageevent's impact and IED alarm thresholds are concerned.

In embodiments of this disclosure, a IED device's voltage magnitudealarm threshold may be initially configured with a reasonable setpointvalue (e.g., the load's rated voltage±5%). The corresponding durationthreshold may be initially configured to zero seconds (highest durationsensitivity). Alternatively, the IED device's voltage magnitude alarmthreshold may be configured for ANY voltage excursion above or below theload's rated voltage (highest magnitude sensitivity). Again, thecorresponding duration threshold (alarm hysteresis) may be initiallyconfigured to zero seconds (highest sensitivity).

As the metered voltage deviates beyond the voltage alarm threshold(regardless of its configured setpoint), the IED device may alarm on avoltage disturbance event. The IED may capture characteristics relatedto the voltage event such as voltage magnitude, timestamp, eventduration, relevant pre/during/post-event electrical parameters andcharacteristics, waveform and waveform characteristics, and/or any othermonitoring system indication or parameter the IED is capable ofcapturing (e.g., I/O status positions, relevant time stamps, coincidentdata from other IEDs, etc.).

Voltage events may be evaluated to determine/verify whether a meaningfuldiscrepancy exists between a pre-event electrical parameter's value(e.g., load, energy, phase imbalance, current, etc.) and itscorresponding post-event value. If a discrepancy does not exist(pre-event vs. post-event), the voltage event may be considered to be“non-impactful” meaning there is no indication the energy consumer'soperation and/or equipment was functionally affected by the voltageevent. The voltage event data may still be retained in memory; however,it may be classified as non-impactful to the energy consumer's operationat the point where the IED captured the voltage event. The existingvoltage alarm magnitude and duration threshold setpoints may thenreconfigure to the magnitude and duration of the non-impactful event(i.e., reconfigured to less sensitive setpoints). Ultimately, inembodiments the more severe voltage event that does not indicate anyoperational and/or equipment functional impact at the IED point willbecome the new voltage magnitude and duration threshold for the voltageevent alarms for that respective IED.

If a pre-event vs. post-event discrepancy does exist, the voltage eventmay be considered to be “impactful” meaning there is at least oneindication the energy consumer's operation and/or equipment wasfunctionally affected by the voltage event. The voltage event data maybe retained in memory, including all measured/calculated data andmetrics related to the impactful event (e.g., % impacted, absoluteimpact, voltage magnitude, event duration, etc.). Moreover, additionalrelevant data associated with the voltage event may be appended to thevoltage event data record/file at a later time (e.g., calculatedrecovery time from event, additional voltage event information fromother IEDs, determined event source location, metadata, IED data, otherelectrical parameters, updated historical norms, statistical analysis,etc.). Because the voltage event is determined to be “impactful,” thevoltage alarm magnitude and duration threshold setpoints are leftunchanged to ensure less severe, yet still impactful, events continue tobe captured by the IED at its respective installation point within theelectrical system.

In embodiments, the final result of this process is the discrete IEDdevice produces a custom voltage alarm template at the point ofinstallation that indicates voltage events (and their respectivecharacteristics) producing impactful events and/or differentiatesimpactful voltage events from non-impactful voltage events. As morevoltage events occur, the custom voltage alarm template more accuratelyrepresents the true voltage event sensitivity at the IED's point ofinstallation. In embodiments, it is possible to capture any (orsubstantially any) voltage event that exceeds any standardized or customthreshold; however, the energy consumers may choose to prioritizeimpactful events as a distinctive category of alarms/indicators. Thiscould be used, for example, to minimize the inundation of superfluousvoltage alarms in the energy consumer's monitoring system byannunciating only prioritized alarms considered to indicate an impactfulhad occurred.

As indicated above in connection with other embodiments of thisdisclosure, the tailored voltage tolerance curve built for customizedvoltage event alarm annunciation could also be used to recommendmitigation equipment to improve ride-through characteristics at theIED's point of installation. Should the energy consumer installmitigation equipment, a manual or automatic indication can beprovided/detected by the system so a new version of the voltagetolerance template can be created based on the system modification(e.g., mitigation equipment installation). In embodiments, a practicalapproach may be a manual indication of supplemental mitigation equipmentbeing added to the system; however, an automatic indication could alsobe provided based on “uncharacteristic changes” in the electricalsystem's response to voltage events at the point of the IED'sinstallation, for example. These “uncharacteristic changes” could beestablished, for example, by statistically evaluating (e.g., viaanalytics algorithms) one or more electrical parameters (i.e., voltage,current, impedance, load, waveform distortion, and so forth). Inembodiments, they may also be identified by any sudden change in voltageevent ride through at the point of the IED's installation. A query maybe made of the energy consumer or electrical system manager to validateany additions, eliminations or changes to the electrical network.Feedback from the energy consumer could be used to better refine anystatistical evaluations (e.g., analytics algorithms) related to voltageevents (or other metering features). Historical information (includingcustomized voltage tolerance curves) would be retained for numerousassessments such as verification of the effectiveness of mitigationtechniques, impact of new equipment installation to voltage ride-throughcharacteristics, and so forth.

As part of this embodiment, more than two event parameters may be usedto configure thresholds to trigger alarms for voltage events. In thedescription above, the magnitude of voltage deviation and the durationof the voltage event are used configure and trigger voltage eventalarms. In embodiments, it is also possible to include more dimensionssuch as load impact and/or recovery time to configure voltage eventalarms. Just as it is possible to set voltage event setpoint thresholdsto alarms only when any load is impacted, it is also possible toconfigure voltage event setpoint thresholds to allow some level ofimpact to the load. Through load identification, either manually orautomatically (based on electrical parameter recognition), it ispossible to alarm when only certain types of loads experience an impactdue to a voltage event. For example, some loads have certain signaturessuch as elevated levels of specific harmonic frequencies. Inembodiments, it would be possible to trigger a voltage event alarm ifthose specific harmonic frequencies are no longer evident.

It is possible to use other parameters to customize the alarm templates.For example, the energy consumer may only be interested in voltageevents with a recovery times greater than 5 minutes. Voltage eventcharacteristics that typically produce recovery times shorter than 5minutes could be filtered by using historical event data to configurethe alarm templates accordingly. Moreover, energy consumers may only beinterested in voltage events that generate monetary losses greater than$500. Again, voltage event characteristics that typically producemonetary losses less than $500 could be filtered using historical datato configure the alarm templates accordingly. As is apparent, any otheruseful parameter derived from voltage event characteristics may besimilarly used to tailor and provide practical alarm configurations.Multiple parameters may also be concurrently used (e.g., recoverytimes >5 minutes AND monetary losses >$500) to provide more complexalarm schemes and templates, and so forth.

In embodiments, as more voltage events occur, additional voltagepre/during/post-event attributes and parameters are captured at both thediscrete and system level and integrated into typical historicalcharacterizations (historical norms). This additional characterizationof voltage events can be used, for example, to estimate/predict theexpected recovery time from both a discrete and system level.Additionally, recommendations can be made to energy consumers on how toachieve a faster recovery time based on historical event data regardingthe effective sequencing to reenergize loads.

In embodiments, customer alarm prioritization can be performed (forvoltage events or any other event type) based on the level of loadmeasured at one or more discrete metering/IED points within theelectrical system. When some indication is received from ametered/virtual/IED point that a load or loads have changed (or areoperating atypically), voltage event alarm setpoint thresholds may bereevaluated and modified based on the level of load measured at one ormore discrete (or based on the load's atypical operation). For example,it may be advantageous to null, silence or deprioritize the voltageevent alarm when one or more IEDs indicate the measure load is low(indicating the facility is off-line). Conversely, raising the priorityof the voltage event alarm would be prudent as one or more IEDs indicateadditional loads being started.

As mentioned earlier in this section, in embodiments it is possible touse this feature to prioritize alarms (including voltage event alarms).The IED may be configured to capture data related to substantially anyperceptible voltage variation from the nominal voltage (or load(s) ratedvoltage) at the point of installation, and take an action(s) includingstoring, processing, analyzing, displaying, controlling, aggregating,and so forth. Additionally, the same action(s) may be performed onsubstantially any alarms (including voltage event alarms) that exceedsome pre-defined setpoint/threshold such as those defined by a dynamicvoltage tolerance curve, standard(s), or other recommendations (asderived from any number or combination of electrical parameters, I/O,metadata, IED characteristics, etc.). In embodiments, any or allcaptured events (including voltage events) may then be analyzed toautomatically prioritize the alarms at a discrete, zone and/or systemlevel based on any number of parameters including: alarm type, alarmdescription, alarm time, alarm magnitude, affected phase(s), alarmduration, recovery time, waveform characteristics, load impactassociated with an alarm, location, hierarchical aspects, metadata, IEDcharacteristics, load type, customer type, economic aspects, relativeimportance to operation or load, and/or any other variable, parameter orcombination thereof related to the event (including voltage events) andthe energy consumer's operation. Prioritizing may be relevant for theinherent characteristics of discrete events or involve comparisons ofmore than one event (including voltage events), and may be performed asevents originate, deferred to a later time, or dependent on theaforementioned parameters. In embodiments, prioritization may beinteractive with the energy consumer, automated, or both with the goalbeing to facilitate the energy consumer's preferences.

In embodiments, parameters to be considered may include at leastelectrical data (from at least one phase), control data, time data,metadata, IED data, operational data, customer data, load data,configuration and installation data, energy consumer preferences,historical data, statistical and analytical data, economic data,material data, any derived/developed data, and so forth.

For example, FIG. 34 illustrates a relatively simple voltage tolerancecurve for an IED with voltage alarm thresholds set at ±10% of thenominal voltage for events arbitrarily ranging from 1 usec tosteady-state. In FIG. 35, a voltage sag event occurs on this IED thatsags to 50% of the nominal voltage and lasts for 3 milliseconds induration. Pre/during/post-event analysis of this event indicates no loadwas impacted. In embodiments, because no load was impacted, the alarmsetpoint thresholds in the IED are reconfigured to indicate/prioritizethe occurrence of a voltage event when (sometimes, only when) themagnitude and duration of a voltage event are more severe than the eventdescribed in FIG. 35. FIG. 36 illustrates changes made to the originalvoltage-tolerance curve. In short, voltage events occurring in the redarea of the graph are expected to be non-impactful and voltage eventsoccurring in the green area of the graph may or may not be impactful. InFIG. 37, another voltage event occurs and is captured by the same IED.In this second voltage event, a voltage interruption (to 0% of thenominal voltage) occurs and lasts for 1 millisecond in duration. Again,pre/during/post-event analysis of the second event indicates no load wasimpacted. And again, the alarm setpoint thresholds in the IED arereconfigured to indicate/prioritize the occurrence of a voltage eventwhen (sometimes, only when) the magnitude and duration of the voltageevent are more severe than the event described in FIG. 36. FIG. 38illustrates changes made to the original voltage-tolerance curve.

In FIG. 39, a third voltage event occurs and is captured by the IED. Inthis third voltage event, the voltage sags to 30% of the nominal voltageand lasts for 2 milliseconds in duration. This time thepre/during/post-event analysis of the third event indicates 25% of theload was impacted. Subsequently, the alarms setpoint thresholds are leftunchanged because of the 25% impact to the load (i.e., a load impactoccurred where it was expected to occur). FIG. 40 illustrates the finalsettings of the voltage event alarm threshold after these three voltageevents. Note that the third event is not shown on the graph because thepurpose of this embodiment of the disclosure is to reconfigure/modifyvoltage event setpoint thresholds. The energy consumer may be notifiedof the third event occurrence, and the voltage event data, calculations,derivation and any analyses may be stored for future reference/benefits.

V. Evaluating and Quantifying Voltage Event Impact on Energy and Demand

Establishing the losses incurred due to voltage events is oftencomplicated; however, embodiments of this disclosure provide aninteresting metric (or metrics) to help quantify the energy and demandcontribution to the total losses. When a voltage event occurs, facilityprocesses and/or equipment may trip off-line. The activity of restartingprocesses and/or equipment consumes energy and can (in some cases)produce a peak demand for the facility. Although these costs arefrequently overlooked, they may be considerable over time whilecontributing little to the actual production and profitability of afacility's operation. There may be ways to recoup some of these coststhrough insurance policy coverage, tax write-offs in some jurisdictions,and even peak demand “forgiveness” from the utility. Perhaps mostimportantly, quantifying the financial impact of voltage events toutility bills can provide incentives to mitigate the voltage eventsleading to these unexpected and potentially impactful losses.

When a voltage event occurs, the analyses described above may beperformed to determine the level of impact to the load or operation. Ifno evidence is found of an impact on a load, process, and/or system,this aspect of this embodiment of the disclosure may be disregarded. Ifthe voltage event is found to have impacted a load, process, and/orsystem, the pre/during/post-event analyses of electrical parameters areperformed. The recovery time clock starts and this embodiment of thedisclosure categorizes the energy consumption, demand, power factor, andany other parameter related to the utility billing structure asassociated with the recovery time interval. Evaluation and analyses maybe performed on these parameters to determine discrete, zonal and/orsystem metrics (including aggregation), comparisons to historical eventmetrics, incremental energy/demand/power factor costs and so forth.These metrics may be evaluated against local utility rate structures tocalculate the total energy-related costs for recovery, discrete, zonal,and/or systems most susceptible and most costly during the recoveryperiod for targeted mitigation, expectations based on historical voltageevent data (e.g., number of events, recovery period of events, energycosts for events, etc.), opportunities to operationally/procedurallyimprove voltage event response time, and so forth.

In embodiments, the data and analyses collected before, during and/orafter the recovery period may be filtered, truncated, summarized, etc.to help the energy consumer better understand the impact of the voltageevent (or other event) on their electrical system, processes, operation,response time, procedures, costs, equipment, productivity or any otherrelevant aspect of their business's operation. It can also provide auseful summary (or detailed report) for discussions with utilities,management, engineering, maintenance, accounting/budgeting, or any otherinterested party.

VI. Disaggregation of Typical and Atypical Operational Data UsingRecovery Time

It is important to recognize a facility's operation during a recoveryperiod is often aberrant or atypical as compared to non-recovery times(i.e., normal operation). It is useful to identify, “tag” (i.e.,denote), and/or differentiate aberrant or atypical operational data fromnormal operational data (i.e., non-recovery data) for performingcalculations, metrics, analytics, statistical evaluations, and so forth.Metering/monitoring systems do not inherently differentiate aberrantoperational data from normal operational data. Differentiating andtagging operational data as either aberrant (i.e., due to being inrecovery mode) or normal provides several advantages including, but notlimited to:

-   -   1. Analyses (such as the aforementioned) may assume operational        uniformity throughout all the data; however, it is useful to        disaggregate aberrant or atypical operational patterns from        normal operational patterns to better evaluate and understand        the significance of the data being analyzed. Data analysis is        improved by providing two different categories of operations;        normal and aberrant/abnormal/atypical. Each may be analyzed        automatically and independently to provide unique and/or more        precise information regarding each operational mode within a        facility or system. Differentiating normal operational data from        atypical operational data (i.e., due to a voltage event) further        bolsters decisions made based on the conclusions of analyses.    -   2. Differentiating normal and aberrant operational modes makes        it possible to provide discrete baseline information for each        operational mode. This provides the ability to better normalize        operation data because atypical data can be excluded from        analysis of system data. Additionally, aberrant operational        modes may be analyzed to help understand, quantify and        ultimately mitigate impacts associated with impactful voltage        events. In the case of event mitigation, data analysis of        aberrant operational periods will help identify possible more        effective and/or economical approaches to reducing the impact of        voltage events.    -   3. Losses incurred due to voltage events are generally difficult        to establish; however, evaluations of data tagged (i.e.,        partitioned, denoted, etc.) as abnormal/aberrant/atypical may be        used to identify energy consumption outliers associated with        voltage events. This information may be used to help quantify        the energy and demand contribution of events to the total        losses. When a voltage event occurs, equipment may        unintentionally trip off-line. The process of restarting        equipment and processes consumes energy and can (in some cases)        produce a new peak demand for the facility. Although these costs        are frequently overlooked/missed, they may be considerable over        time while contributing little to the actual production and        profitability of the operation. There may be ways to recoup some        of these costs through insurance policy coverage, tax write-offs        in some jurisdictions, and even peak demand “forgiveness” from        the utility. Perhaps most importantly, quantifying the financial        impact of voltage events to utility bills can provide incentive        to mitigate the voltage events leading to these unexpected and        potentially impactful losses.

VII. Other Evaluations and Metrics Related to Voltage Event Impact andRecovery Time

As is known, voltage events including outages are a leading global causeof business interruption-related losses. The annual estimated economicloss for medium and large businesses is estimated to be between $104billion and $164 billion based on a study by Allianz Global. Inembodiments, by incorporating additional economic metadata, it ispossible to evaluate individual voltage events to determine the monetaryimpact of these events. Additionally, in embodiments it is possible tototalize the voltage event impacts by aggregating data and informationfrom individual events. Some example useful financial information tohelp quantify the economic impact of voltage events include: averagematerial loss/event/hour, utility rate tariffs (as discussed above),average production loss cost/event/hour, estimated equipmentloss/event/hour, average 3^(rd) party costs/event/hour, or any othermonetary metric related to the cost of downtime on a per event ordaily/hourly/minutely basis. Using the recovery time from thecalculations described above, metrics may be determined forsubstantially any loss that has been monetarily quantified. Thesemetrics may be determined at a discrete IED, zone and/or system levelaccordingly.

A number of new voltage event-related indices are set forth herein asuseful metrics for qualifying and quantifying voltage events andanomalies. While these new indices focus on voltage sags, in embodimentsthey may also be considered for any other voltage event or category ofpower quality event. Example indices include:

-   -   Mean Time Between Events (MTBE). As used herein, the term “MTBE”        is used to describe the average or expected time a system or        portion of a system is operational between events and their        subsequent recovery time. This includes both impactful and        non-impactful events, so there may or may not be a quantity of        recovery time associated with each event.    -   Mean Time Between Impactful Events (MTBIE). As used herein, the        term “MTBIE” is used to describe the average or expected time a        system or portion of a system is operational between events and        their subsequent recovery time. In embodiment, this metric is        limited to only impactful events and will likely have some        quantity of recovery time associated with each event.    -   Mean Time to Restart (MTTR). As used herein, the term “MTTR” is        used to describe the average time it takes to restart production        at a system or portion of a system (e.g., load, zone, etc.)        level. This “average time” includes all (or substantially all)        factors involved in restarting production including (but not        limited to): repairs, reconfigurations, resets,        reinitializations, reviews, retests, recalibrations, restarts,        replacing, retraining, relocating, revalidations, and any other        aspect/function/work effecting the recovery time of an        operation.    -   Sag rate. As used herein, the term “sag rate” is used to        describe the average number of sag-events of a system or portion        of a system over a given time period such as hours, months,        years (or other time period).    -   Production Availability. As used herein, the term “production        availability” generally refers to the immediate readiness for        production, and is defined as the ability of a facility to        perform its required operation at a given time or period. This        metric focuses on event-driven parameter(s) and may be        determined by:

${PA}_{i} = \frac{MTBIE}{{MTBIE} + {MTTR}}$

In embodiments, systems, zones, and/or discrete IED points may becharacterized by their “Number of 9's Production Up-Time,” which is anindication of the production availability exclusive of the recovery timeduration. Similar to the number of 9's in the usual connotation, thismetric may be determined annually (or normalized to an annual value) toprovide an indication or metric of the impact of voltage events (orother events) on an operation's productivity. This metric may be usefulto help identify mitigation investment opportunities and to prioritizethose opportunities accordingly.

In embodiments, it is possible to use the metrics set forth above toestimate/predict recovery time based on historical recovery timeinformation. A voltage event's magnitude, duration, location, metadata,IED characterization, or other calculated/derived data and information,for example, may be used to facilitate these estimations andpredictions. This measure may be performed and provided to energyconsumers at the discrete IED point, zone, and/or system level as one ormore reports, texts, emails, audible indications, screens/displays, orthrough any other interactive means.

A few examples of supplementary metrics that may be unique to an energyconsumer's operation and assist in prioritizing mitigation equipmentconsiderations for placement, investment, etc. include:

-   -   Average Zonal Interruption Frequency Index (AZIFI). AZIFI is an        example metric that can be used to quantify zones experiencing        “the most” interruptions in an electrical system. As used        herein, AZIFI is defined as:

${AZIFI} = \frac{{number}\mspace{14mu} {of}\mspace{14mu} {zone}\mspace{14mu} {impacts}\mspace{14mu} {within}\mspace{14mu} {facility}}{{total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {zones}\mspace{14mu} {in}\mspace{14mu} {the}\mspace{14mu} {system}}$

-   -   Zonal Impact Average Interruption Frequency Index (ZIAIFI).        ZIAIFI is an example metric that can be used to show trends in        zone interruptions along with number of zones affected in        electrical system. As used herein, ZIAIFI is defined as:

${ZIAIFI} = \frac{{number}\mspace{14mu} {of}\mspace{14mu} {zone}\mspace{14mu} {impacts}}{{number}\mspace{14mu} {of}\mspace{14mu} {zones}\mspace{14mu} {that}\mspace{14mu} {had}\mspace{14mu} {at}\mspace{14mu} {least}\mspace{14mu} {one}\mspace{14mu} {impact}}$

-   -   Average Zonal Interruption Duration Index (AZIDI). AZIDI is an        example metric that can be used to indicate an overall        reliability of the system based on an average of zone impacts.        As used herein, AZIDI is defined as:

${AZIDI} = \frac{{sum}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {recovery}\mspace{14mu} {time}\mspace{14mu} {durations}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {impacted}\mspace{14mu} {zones}}{{total}\mspace{14mu} {number}\mspace{14mu} {of}\mspace{14mu} {zones}\mspace{14mu} {in}\mspace{14mu} {the}\mspace{14mu} {system}}$

-   -   Zonal Total Average Interruption Duration Index (ZTAIDI). ZTAIDI        is an example metric that can be used to provide an indication        of the average recovery period for zones that experienced at        least one impactful voltage event. As used herein, ZTAIDI is        defined as:

${ZTAIDI} = \frac{{sum}\mspace{14mu} {of}\mspace{14mu} {the}\mspace{14mu} {durations}\mspace{14mu} {of}\mspace{14mu} {all}\mspace{14mu} {zone}\mspace{14mu} {impacts}}{{number}\mspace{14mu} {of}\mspace{14mu} {zones}\mspace{14mu} {that}\mspace{14mu} {experienced}\mspace{14mu} {at}\mspace{14mu} {least}\mspace{14mu} {one}\mspace{14mu} {impact}}$

While the foregoing metrics focus on zone-related impacts, inembodiments some or all concepts may be reused for discrete IED pointsor (in some cases) system impact metrics. It is understood that thepurpose here is to document examples of the ability to create usefulmetrics for energy consumers and their operations; not to define everypossible metric or combination thereof.

It is also understood that each of the metrics discussed above may befurther determined and partitioned for upstream, downstream, internal(e.g., facility), and external (e.g., utility) voltage event sources asappropriate. The latter two mentioned (internal/external) may requiresome level of hierarchical classification of the IED and/or electricalsystem. Other classifications of hierarchies (e.g., protection layoutschemes, separately derived sources, processes or sub-systems, loadtypes, sub-billing groups or tenants, network communications schemes,etc.) may be used to create/derive additional useful metrics as neededto better evaluate the impact of voltage events on a facility'soperation, for example. Outputs from embodiments of this disclosure maybe provided by one or more reports, texts, emails, audible indications,screens/displays, or through any other interactive means. Indicationsmay be provided at the IED, on-site software, cloud, gateway, or othermonitoring system component and/or accessory. In embodiments, theoutputs and indications may be generated by circuitry and systemsaccording to the disclosure in response to the circuitry and systemsreceiving and processing respective inputs.

VIII. Voltage Event Recovery Status Tracking

An example method according to the disclosure for reducing recovery timeperiods includes providing a method of tracking the recovery as itprogresses. By identifying and monitoring the recovery periods throughdiscrete IEDs, zones, hierarchies, and/or the system in real-time, theenergy consumer (and the systems and methods disclosure herein) is/arebetter able to identify, manage, and expedite the recovery process foran event throughout their facility. Event recovery tracking allowsenergy consumers to understand the status of the recovery and makebetter and quicker decisions to facilitate its recovery. This embodimentwould also allow the energy consumer to review historical data to makerecovery improvements, produce and/or update recovery procedures,identify zonal recovery constrictions, troublesome equipment, and soforth to improve future event recovery situations (and thus, increasesystem uptime and availability). Alarming capabilities may beincorporated into recovery situations to provide indications ofconstraining locations within zones or the facility. Historical recoverymetrics or some other configured setpoints may be used to determinerecovery alarm threshold settings for IEDs, system software, and/orcloud, and outputs from embodiments of this disclosure may be providedby one or more reports, texts, emails, audible indications,screens/displays, or through any other interactive means.

IX. Developing Various Baselines Related to Voltage Events

Another example method for determining expected recovery times usesfactors such as market segments and/or customer types, processes-basedevaluations, and/or load and equipment types to determine the expectedrecovery times. By defining recovery times based on these and otherfactors, for example, a recovery time baseline or reference can bedeveloped with respect to a voltage event's magnitude, duration, percentload impacted, and/or any other electrical parameter, metadata, or IEDspecification. The baselines/references may be used to set recoveryalarm thresholds, assess recovery time performance and identifyopportunities for improvement, estimate actual vs. expected recoverytime and costs, improve accuracy of estimates for impactful voltageevents, and so forth. Actual historical voltage event impact andrecovery time data may be used to produce relevant models throughvarious means including statistical analyses (and/or analytics) andevaluations, simple interpolation/extrapolation, and/or any other methodthat produces a reasonable typical value(s). Baseline/reference modelsmay range from simple to complex, and may be created or determined fordiscrete IED locations, zones, or entire systems, and outputs fromembodiments of this disclosure may be provided by one or more reports,texts, emails, audible indications, screens/displays, or through anyother interactive means.

X. Evaluating Voltage Event for Similarities to Identify RepetitiveBehavior

In embodiments, evaluating voltage events across an electrical system toexamine event similarity may be useful for energy consumers.Similarities may be in event time of occurrence, seasonality, recoverytime characteristics, behavior of electrical parameters, behavior ofzonal characteristics, behavior of operational processes, and/or anyother notable behaviors or commonalities. Identifying repetitivebehaviors and/or commonalities may be an important tactic forprioritizing and resolving voltage event effects. Moreover,analysis/analytics of historical data may provide the ability to predictthe system impact and recovery time due to a voltage event after theinitial onset of said voltage event.

XI. Voltage Event Forecasting

As mentioned in previous embodiments of the disclosure, it is importantto be able to identify beneficial opportunities for energy consumers tomitigate voltage events. Another metric that may be considered isforecasting an estimated number of interruptions, estimated impact, andtotal recovery time (and associated costs). In embodiments, this metricmay be extremely useful for planning purposes, support of capitalinvestment opportunities in voltage event mitigation equipment, and evento forecast expected savings for installing said mitigation equipment.These forecasts may be evaluated at a later point in time to ascertaintheir accuracy and to fine-tune forecasts and expectations goingforward.

XII. Other Graphs and Diagrams Related to Voltage Event Impact andRecovery Time

Aside from the various plots (or graphs) discussed in connection withthe embodiments described above, there are other additional usefulmethods to display data related to voltage events. The graphs describedbelow in connection with FIGS. 41-44, for example, are only a fewexamples of displaying data in a useful format; there may be many othermethods to present voltage event data in a meaningful way that canbenefit energy consumers. Graphs, charts, tables, diagrams, and/or otherillustrative techniques, for example, may be used to summarize, compare,contrast, validate, order, trend, demonstrate relationships, explain,and so forth. These data types may be real-time, historical, modeled,projected, baseline, measured, calculated, statistical, derived,summarized, and/or estimated. Graphs may also be any dimension (e.g.,2-D, 3-D, etc.), color, shade, shape (e.g., line, bar, etc.), etc. toprovide a unique and useful perspective.

FIG. 41 illustrates an example of the load impact versus recovery timefor a single event. The green area is indicative of normal or expectedrange of operational parameters, the shaded orange area is highlightingthe recovery time period, and the black line is the load as a functionof time. FIG. 42 illustrates an example of a series of impactful eventsversus their recovery time from a single IED (multiple IEDs could alsobe used here). In this example, the green area is indicative of normalor expected operational parameters, and the shaded orange highlights theperiods when the system has experienced an impactful event andexperienced a recovery period. FIG. 43 illustrates an example ofadditional data being integrated with the data shown in FIG. 41. In thisexample, the green area is indicative of normal or expected range ofoperational parameters, the shaded orange is highlighting the recoverytime period, the black line is showing the load as a function of time,the dashed pink line is showing the expected load as a function of time,and the dashed blue line shows a typical pre-event profile. As a rule ofthumb, the behavior of upstream events may be more unpredictable thandownstream events over time. FIG. 44 illustrates an example ofpre/during/post-event percent of load impact versus recovery time for avoltage event. Again, different variables, metrics parameters,characteristics, etc. may be graphed, illustrated, etc. shown as neededor useful.

XIII. Aggregation/Consolidation of Voltage Event Impact and RecoveryTime Data

As is known, voltage events are often extensive, impacting multipleloads, processes, and even the entire system concurrently. Inembodiments, metering systems according to the disclosure may exhibitmultiple alarms from different IEDs located across the facility. Sourceevents generally impact the entire system, for example, resulting inevery (or substantially every) capable IEDs indicating an event hasoccurred.

In embodiments, aggregating/consolidating the multitude of voltage eventdata, alarms and impacts across a system is important for severalreasons. First, many energy consumers have a tendency to ignore “alarmavalanches” from monitoring systems, so aggregating/consolidatingvoltage event data decreases the number alarms the energy consumer hasto review and acknowledge. Second, the data from a flurry of alarms isoften the result of one voltage event coming from the same root cause.In this case, it is much more efficient to reconcile all coincidentvoltage events captured by multiple IEDs into a single event forreconciliation. Third, bundled voltage events are much easier to analyzethan independent voltage events as most of the relevant data andinformation is available in one place. For the sake of brevity, thereare many other reasons to aggregate/consolidate voltage events notlisted here.

The ability to aggregate/consolidate the impact of voltage events andtheir often-accompanying recovery times is important because it helpsavoid redundancy of event data. Redundant event data can skew metricsand exaggerate conclusions, which may result in flawed decisions. Thisdisclosure focuses on three layers of aggregation/consolidation withinelectrical systems: IED, zonal and system.

In embodiments, the first layer (IED) requires minimalaggregation/consolidation because data is acquired from a singlepoint/device and (hopefully) the device shouldn't be producing redundantinformation within itself from voltage events. In some cases, there maybe somewhat superfluous alarm information from a single device. Forexample, a three-phase voltage event may provide one alarm for each ofthe three phases experiencing the voltage event. Moreover, an alarm maybe triggered for both the event pickup and dropout, resulting in sixtotal voltage event alarms (a pickup and dropout alarm for each of thethree phases). While this example of alarm abundance may be bothersomeand confusing, many devices and monitoring systems alreadyaggregate/consolidate multiple event alarms as just described into asingle event alarm. In some embodiments, a single voltage event alarmmay be provided from each IED for each voltage event that occurs in theelectrical system.

It was mentioned above that a voltage event often impacts multiple IEDswithin a monitoring system; specifically, those that are capable ofcapturing anomalous voltage conditions. Since zones and systemstypically consist of multiple IEDs, the need to aggregate/consolidatethe impact and subsequent repercussions of voltage events lies withthese two (zones and systems). Although a zone may encompass an entiresystem, zones are configured as a subset/sub-system of the electricaland/or metering system. However, because zones and systems bothgenerally consist of multiple devices, they will be treated similarly.

In embodiments, there are different methods/techniques toaggregate/consolidate zones. A first example method includes evaluatingthe voltage event impact and recovery time from all IEDs within aparticular zone and attributing the most severe impact and recovery timefrom any single IED within that zone to the entire zone. Because theevent impact and recovery time are independent variables, and thereforemay be derived from different IEDs, these two variables should betreated independently from each other. Of course, it would be importantto track which zonal device was considered/recognized as havingexperienced the most severe impact and which zonal device experiencedthe longest recovery time. This same approach could be used for systemsby leveraging the conclusions generated from the zone evaluations.Ultimately, the recovery time for a system is not completed until allrelevant IEDs indicate that is the case.

A second example method includes assessing a voltage event within a zoneby using statistical evaluations (e.g., average, impact and averagerecovery time, etc.) from all IEDs with a particular zone. In this case,the severity of a voltage event may be determined for the entire zone bystatistically appraising data from all IEDs and providing results torepresent the entire zone for each particular voltage event. Statisticaldeterminations including means, standard deviations, correlations,confidence, error, accuracy, precision, bias, coefficients of variation,and any other statistical methods and/or techniques may be employed toaggregate/consolidate the data from multiple IEDs to a representativevalue or values for the zone. The same statistical approach may be usedto combine zones into representative metrics/values for system impactand recovery time. Again, the recovery time for a system will becontingent on each relevant IED indicating that is the case.

Another example method to evaluate voltage events is by load-type. Inembodiments, the energy consumer (or systems and method disclosedherein) may choose to categorize and aggregate/consolidate loads bysimilarity (e.g., motors, lighting, etc.) regardless of their locationwithin the facility's electrical system, and evaluate the impact andrecovery time of those loads accordingly. It would also be possible toevaluate voltage events by their respective processes. Byaggregating/consolidating loads (regardless of type, location, etc.)associated with the same process, the impact and recovery time could bequantified for said process. Another method to aggregate/consolidatevoltage events is by sources and/or separately derived sources. Thisapproach would help quantify the impact and recovery time of a voltageevent as it related to the energy source within the facility (or out onthe utility network). Other useful and logical methods toaggregate/consolidate voltage event information from two or more IEDsmay be considered as well (e.g., by building, by product, by cost, bymaintenance, and so forth).

In embodiments, a fundamental purpose of aggregating/consolidatingvoltage event data is to identify opportunities to decrease theseevents' overall impact on the energy consumer's business to reducedowntime and make it more profitable. One or more of the methods (orcombinations of methods) described herein may be used to meet thisobjective. It may be useful or even required to have one or more ofthese methods configured by the energy consumer (or surrogate), or thesystem and methods disclosed herein. The ability to consider the voltageevent impact and recovery time at discrete IEDs is not mutuallyexclusive from any approach to consider and evaluateaggregated/consolidated voltage event impact and recovery time.

Another interesting prospect would be evaluating the performance of theenergy consumer's operation after the initial voltage event occurs. Forexample, a voltage event may result in one load tripping off-line.Shortly after, another related load may also trip off-line as a resultof the first load tripping; not due to another voltage event. The extentof this chain reaction/propagation would be of interest when determiningconsequences of providing ride-through mitigation for the first load. Inthis example, providing a timeline of load reactions over the recoveryperiod due to the original voltage event may be prudent to help minimizethe overall impact of voltage events on the energy consumer's operation.

In embodiments, outcomes from analyses of the voltage and current dataapply to the point in the network where the IED capturing the data isconnected. Each IED in the network may typically yield distinct analysesof the event, assuming each IED is uniquely placed. As used herein, theterm “uniquely placed” generally refers to the location of theinstallation within the electrical system, which impacts impedance,metered/connected loads, voltage levels, and so forth. In some cases, itmay be possible to interpolate or extrapolate voltage event data on acase-by-case basis.

In embodiments, in order to accurately characterize power quality events(e.g., voltage sags) and their subsequent network impact(s), it isimportant to measure the voltage and current signals associated with theevent. Energy-related signals (e.g., voltage signals) can be used tocharacterize the event, current signals can be used to quantify theevent's impact, and both voltage and current can be used to derive otherrelevant electrical parameters related to this disclosure. Althoughoutcomes from analyses of the voltage and current data apply to thepoint in the network where the IED capturing the data is connected, itmay be possible to interpolate and/or extrapolate voltage event data ona case-by-case basis. Each IED in the network typically yields distinctanalyses of the event, assuming each IED is uniquely placed.

In embodiments, there are multiple factors that can influence the impact(or non-impact) of a voltage sag. The impedance of the energy consumer'selectrical system may cause voltage events to produce more severevoltage sags deeper into the system hierarchy (assuming a radial-fedtopology). Voltage event magnitudes, durations, fault types, operationalparameters, event timing, phase angles, load types, and a variety ofother factors related to functional, electrical, and even maintenanceparameters can influence the effects of voltage sag events.

It is understood that any relevant information and/or data derived fromIEDs, customer types, market segment types, load types, IEDcapabilities, and any other metadata may be stored, analyzed, displayed,and/or processed in the cloud, on-site (software and/or gateways), or ina IED in some embodiments.

Referring to FIGS. 45-48, several flowcharts (or flow diagrams) areshown to illustrate various methods of the disclosure. Rectangularelements (typified by element 4505 in FIG. 45), as may be referred toherein as “processing blocks,” may represent computer software and/orIED algorithm instructions or groups of instructions. Diamond shapedelements (typified by element 4525 in FIG. 45), as may be referred toherein as “decision blocks,” represent computer software and/or IEDalgorithm instructions, or groups of instructions, which affect theexecution of the computer software and/or IED algorithm instructionsrepresented by the processing blocks. The processing blocks and decisionblocks can represent steps performed by functionally equivalent circuitssuch as a digital signal processor circuit or an application specificintegrated circuit (ASIC).

The flowcharts do not depict the syntax of any particular programminglanguage. Rather, the flowcharts illustrate the functional informationone of ordinary skill in the art requires to fabricate circuits or togenerate computer software to perform the processing required of theparticular apparatus. It should be noted that many routine programelements, such as initialization of loops and variables and the use oftemporary variables are not shown. It will be appreciated by those ofordinary skill in the art that unless otherwise indicated herein, theparticular sequence of blocks described is illustrative only and can bevaried. Thus, unless otherwise stated, the blocks described below areunordered; meaning that, when possible, the blocks can be performed inany convenient or desirable order including that sequential blocks canbe performed simultaneously and vice versa. It will also be understoodthat various features from the flowcharts described below may becombined in some embodiments. Thus, unless otherwise stated, featuresfrom one of the flowcharts described below may be combined with featuresof other ones of the flowcharts described below, for example, to capturethe various advantages and aspects of systems and methods associatedwith dynamic tolerance curves sought to be protected by this disclosure.

Referring to FIG. 45, a flowchart illustrates an example method 4500 formanaging power quality events (or disturbances) in an electrical systemthat can be implemented, for example, on a processor of an IED (e.g.,121, shown in FIG. 1A) and/or a processor of a control system associatedwith the electrical system. Method 4500 may also be implemented remotefrom the IED and/or control system in a gateway, cloud, on-sitesoftware, etc.

As illustrated in FIG. 45, the method 4500 begins at block 4505, wherevoltage and/or current signals (or waveforms) associated with one ormore loads (e.g., 111, shown in FIG. 1A) in an electrical system aremeasured and data is captured, collected, stored, etc. by an IED (and/orcontrol system) coupled to the loads.

At block 4510, electrical measurement data from the voltage and/orcurrent signals is processed to identify at least one power qualityevent associated with one or more of the loads. In some embodiments,identifying the at least one power quality event may includeidentifying: (a) a power quality event type of the at least one powerquality event, (b) a magnitude of the at least one power quality event,(c) a duration of the at least one power quality event, and/or (d) alocation of the at least one power quality event in the electricalsystem, for example. In embodiments, the power quality event type mayinclude one of a voltage sag, a voltage swell, and a voltage transient.

At block 4515, an impact of the at least one identified power qualityevent on one or more of the loads is determined. In some embodiments,determining the impact of the at least one identified power qualityevent includes measuring one or more first parameters (e.g., “pre-event”parameters) associated with the loads at a first time (e.g., a timeprior to the event), measuring one or more second parameters (e.g.,“post-event” parameters) associated with the loads at a second time(e.g., a time after the event), and comparing the first parameters tothe second parameters to determine the impact of the at least oneidentified power quality event on the loads. In embodiments, the powerquality event(s) may be characterized as an impactful event or anon-impactful event based, at least in part, on the determined impact ofthe event(s). An impactful event may, for example, correspond to anevent that interrupts operation (or effectiveness) of the loads and/orthe electrical system including the loads. This, in turn, may impact anoutput of the system, for example, the production, quality, rate, etc.of a product generated by the system. In some embodiments, the productmay be a physical/tangible object (e.g., a widget). Additionally, insome embodiments the product may be a non-physical object (e.g., data orinformation). A non-impactful event, by contrast, may correspond to anevent that does not interrupt (or minimally interrupts) operation (oreffectiveness) of the loads and/or the electrical system including theloads.

At block 4520, the at least one identified power quality event and thedetermined impact of the at least one identified power quality event areused to generate or update an existing tolerance curve associated withthe one or more of the loads. In embodiments, the tolerance curvecharacterizes a tolerance level of the loads to certain power qualityevents. For example, the tolerance curve (e.g., as shown in FIG. 4) maybe generated to indicate a “prohibited region”, a “no damage region” anda “no interruption in function region” associated with the loads (and/orelectrical system), with the respective regions corresponds to varioustolerance levels of the loads to certain power quality events. Thetolerance curve may be displayed on a graphical user interface (GUI)(e.g., 230, shown in FIG. 1B) of the IED and/or or GUI of the controlsystem, for example. In embodiments where a tolerance curve has alreadybeen generated prior to block 4520, for example, due to there being anexisting tolerance curve, the existing tolerance curve may be updated toinclude information derived from the at least one identified powerquality event and the determined impact of the at least one identifiedpower quality event. An existing tolerance curve may exist, for example,in embodiments in which a baseline tolerance curve exists or inembodiments in which a tolerance curve has already been generated usingmethod 4500 (e.g., an initial tolerance curve generated in response to afirst or initial power quality event). In other words, in embodiments anew tolerance curve is typically not generated after each identifiedpower quality event, but rather each identified power quality event mayresult in updates being made to an existing tolerance curve.

At block 4525, which is optional in some embodiments, it is determinedif the impact of the at least one identified power quality event exceedsa threshold or falls outside of a range or region (e.g., “nointerruption in function region”) indicated in the tolerance curve. Ifit is determined that the impact of the at least one identified powerquality event falls outside of the range indicated in the tolerancecurve (e.g., the event results in an interruption to the function of aload as measured by an electrical parameter or indicated by someexternal input), the method may proceed to block 4530. Alternatively, ifis determined that the impact of the at least one identified powerquality event does not fall outside of a range indicated in thetolerance curve (e.g., the event does not result in an interruption in afunction of a load), the method may end in some embodiments. In otherembodiments, the method may return to block 4505 and repeat again. Forexample, in embodiments in which it is desirable to continuously (orsemi-continuously) capture voltage and/or current signals and todynamically update the tolerance curve in response to power qualityevents identified in these captured voltage and/or current signals, themethod may return to block 4505. Alternatively, in embodiments in whichit is desirable to characterize power quality events identified in asingle set of captured voltage and/or current signals, the method mayend.

Further, in embodiments the event information may be used to adjust(e.g., expand) the “no interruption in function” region, for example, togenerate a custom tolerance curve for the specific IED location (similarto FIG. 2). It is to be appreciated that characterizing the electricalsystem at certain points is extremely useful to users because they canbetter understand the behavior of their system.

In some embodiments, the range indicated in the tolerance curve is apredetermined range, for example, a user configured range. In otherembodiments, the range is not predetermined. For example, I may chooseto have no “no interruption in function” region and say anythingdeviating from a nominal voltage needs to be evaluated. In this case,the voltage may range all over the place and I may have dozens of powerquality events; however, my load may not experience any interruptions.Thus, these events are not considered impactful. In this case, Iwiden/expand my “no interruption” region from basically the nominalvoltage outwards to the point where these events do start to perturbatemy loads (based on measured load impact pre-event vs. post event).

In other words, the invention is not limited to the ITIC curve (or anyother predetermined range or curve(s)). Rather, embodiments of theinvention call for “creating” a custom voltage tolerance curve for aspecific location (i.e., where the IED is located) within the electricalsystem or network. The curve may be based on the ITIC curve, the SEMIcurve, or any number of other curves. Additionally, the curve may be acustom curve (i.e., may not be based on a known curve, but rather may bedeveloped without an initial reference or baseline). It is understoodthat a predetermined tolerance curve is not required for this invention,rather it just used to explain the invention (in connection with thisfigure, and in connection with figures described above and below).

At block 4530, which is optional is some embodiments, an actionaffecting at least one component of the electrical system may beautomatically performed in response to the determined impact of the atleast one identified power quality event being outside of the rangeindicted in the tolerance curve. For example, in some embodiments acontrol signal may be generated in response to the determined impact ofthe at least one identified power quality event being outside of therange, and the control signal may be used to affect the at least onecomponent of the electrical system. In some embodiments, the at leastone component of the electrical system corresponds to at least one ofthe loads monitored by the IED. The control signal may be generated bythe IED, a control system, or another device or system associated withthe electrical system. As discussed in figures above, in someembodiments the IED may include or correspond to the control system.Additionally, in some embodiments the control system may include theIED.

As another example, an action that may be affected at block 4530 isstarting and stopping a timer to quantify a length (or duration) of theimpact to production, for example, in a facility with which the impactis associated. This will help a user make better decisions regardingoperation of the facility during atypical conditions.

Subsequent to block 4530, the method may end in some embodiments. Inother embodiments, the method may return to block 4505 and repeat again(for substantially the same reasons discussed above in connection withblock 4525). In some embodiments in which the method ends after block4530, the method may be initiated again in response to user input and/ora control signal, for example.

Referring to FIG. 46, a flowchart illustrates an example method 4600 forquantifying power quality events (or disturbances) in an electricalsystem that can be implemented, for example, on a processor of an IED(e.g., 121, shown in FIG. 1A) and/or a processor of a control system.Method 4600 may also be implemented remote from the IED in a gateway,cloud, on-site software, etc. This method 4600 evaluates voltage and/orcurrent signals measured and captured by the IED to determine whetherthe electrical system was impacted (e.g., at the IED(s) level) usingpre-event/post-event power characteristics. In embodiments, it ispossible to determine a recovery time using a threshold (e.g., thepost-event power is 90% of the pre-event power). This allows us toquantify the impact of a power quality disturbance to a load(s),process(es), system(s), facility(ies), etc.

As illustrated in FIG. 46, the method 4600 begins at block 4605, wherevoltage and/or current signals (or waveforms) are measured and capturedby an IED.

At block 4610, the voltage and/or current signals are processed toidentify a power quality event associated with one or more loads (e.g.,111, shown in FIG. 1A) monitored by the IED. In some embodiments,pre-event, event and post-event logged data may also be used to identifythe power quality event. The pre-event, event and post-event logged datamay, for example, be stored on a memory device associated with the IEDand/or gateway, cloud and/or on-site software application.

At block 4615, pre-event parameters are determined from the voltageand/or current signals. In embodiments, the pre-event parameterscorrespond to substantially any parameters that can be directly measuredand/or derived from voltage and current including, but not limited to,power, energy, harmonics, power factor, frequency, event parameters(e.g., time of disturbance, magnitude of disturbance, etc.), etc. Inembodiments, pre-event data can also be derived from “statisticalnorms.” Metadata may also be used to help derive additional parametersaccordingly.

At block 4620, an impact of the power quality event is determined,measured or calculated. In embodiments, the event impact is calculatedbased on pre-event vs. post-event parameters. In embodiments, thisincludes both the characteristics of the event (i.e., magnitude,duration, disturbance type, etc.) and its impact to load(s),process(es), system(s), facility(ies), etc. at the metered point in thesystem.

At block 4625, recovery thresholds (or conditions) are compared toreal-time parameters. In embodiments, the recovery thresholds maycorrespond to a percent of pre-event conditions to be considered as asystem, sub-system, process, and/or load recovery condition. Inembodiments, industry standards, market segment recommendations,historical analysis, independently determined variables, and/or loadcharacteristics may be used to provide the recovery thresholds.Additionally, statistical norms may be used to provide the recoverythresholds. In embodiments, the recovery thresholds are configured(e.g., pre-configured) recovery thresholds that are stored on a memorydevice associated with the IED. An alternative approach is to pass allvoltage event information to the cloud or on-site software and thenfilter it there using recovery thresholds. In this case, the recoverythresholds would be stored in the cloud or on-site and not in the IED.

At block 4630, the IED determines if the real-time parameters meet therecovery thresholds (or conditions). If the IED determines that thereal-time parameters meet the recovery thresholds, the method proceedsto block 4635. Alternatively, if the IED determines that the real-timeparameters do not meet the recovery thresholds, the method may return toblock 4625, and block 4625 may be repeated again. In embodiments, theoutput here is to determine the recovery time; therefore, it may stay inthe loop until the post-event levels meet a predetermined threshold.

At block 4635, the IED calculates a recovery time from the power qualityevent. In embodiments, the recovery time is calculated from a timeassociated with the power quality event (e.g., an initial occurrence ofthe power quality event) until a time the recovery thresholds are met.

At block 4640, an indication of the power quality disturbance (or event)is provided at an output of the IED. In embodiments, the indication mayinclude one or more reports and/or one or more control signals. Thereport may be generated to include information from any discrete IED ofthe electrical system including: recovery time, impact on power, costsassociated with the event impact, I/O status changes, time of event/timeof recovery, changes in voltages/currents, imbalance changes, areasimpacted, etc. In embodiments, recovery time and impact may be based ondata from one or more IEDs. The reports may be provided to customer,sales teams, offer management, engineering teams, and/or any otherinterested party, etc. The control signals may be generated to controlone or more parameters or characteristics associated with the electricalsystem. As one example, the control signals may be used to adjust one ormore parameters associated with load(s) which the IED is configured tomonitor.

At block 4640, the indication of the power quality disturbance (andother data associated with method 4600) may also be stored. In someembodiments, the indication may be stored locally, for example, on asame site as the IED (or on the IED device itself). Additionally, insome embodiments the indication may be stored remotely, for example, inthe cloud and/or on-site software. After block 4640, the method 4600 mayend.

Referring to FIG. 47, a flowchart illustrates an example method 4700 forexpanded qualified lead generation for power quality. Similar to method4600 described above in connection with FIG. 46, for example, inembodiments method 4700 can be implemented on a processor of an IEDand/or a processor of a control system. Method 4700 may also beimplemented remote from the IED in a gateway, cloud, on-site software,etc. In embodiments, by evaluating pre-event/post-event powercharacteristics of power quality events, it is possible to quantify thesusceptibility of the electrical system at metered points to powerquality disturbances. This information could be used to identify productofferings for mitigative solutions and provide better qualified leads toorganizations marketing those solutions. In embodiments, method 4700 mayalso be used for energy savings opportunities (e.g., power factorcorrection, increased equipment efficiency, etc.) when a power qualityevent occurs.

As illustrated in FIG. 47, the method 4700 begins at block 4705, wherevoltage and/or current signals (or waveforms) are measured and capturedby an IED.

At block 4710, the voltage and/or current signals are processed toidentify a power quality event associated with one or more loadsmonitored by the IED. In some embodiments, pre-event, event andpost-event logged data may also be used to identify the power qualityevent. The pre-event, event and post-event logged data may, for example,be stored on a memory device associated with the IED and/or gateway,cloud and/or on-site software application.

At block 4715, pre-event parameters are determined from the voltageand/or current signals. In embodiments, the pre-event parameterscorrespond to substantially any parameters that can be directly measuredand/or derived from voltage and current including, but not limited to,power, energy, harmonics, power factor, frequency, event parameters(e.g., time of disturbance, magnitude of disturbance, etc.), etc. Inembodiments, pre-event data can also be derived from “statisticalnorms.” Metadata may also be used to help derive additional parametersaccordingly.

At block 4720, an impact of the power quality event is calculated. Inembodiments, the event impact is calculated based on pre-event vs.post-event parameters. In embodiments, this includes both thecharacteristics of the event (i.e., magnitude, duration, disturbancetype, etc.) and its impact to load(s), process(es), system(s),facility(ies), etc. at the metered point in the system.

At block 4725, event characteristics are compared to mitigativesolutions (e.g., product solutions). In embodiments, there may be alibrary of design and applications criteria for solutions to mitigateissues associated with a power quality event or disturbance. The libraryof design and applications criteria for solutions may be stored on amemory device associated with the IED, or accessed by the IED (e.g.,remotely, via the cloud). In some embodiments, block 4725 may beperformed in the cloud or on-site software. That way the energy consumeris able to see everything from a system level.

At block 4730, the IED determines if a particular entity (e.g.,Schneider Electric) provides a mitigative solution for specific event.If the IED determines that the particular entity provides a mitigativesolution for the specific event, the method proceeds to a block 4635.Alternatively, if the IED determines that the particular entity does notprovide a mitigative solution for the specific event, the methodproceeds to a block 4750. In some embodiments, the “IED” may be definedas being in the cloud or on-site (yet remote from the meter). Inembodiments, we may want to put the solutions and much of the analysisin the cloud or on-site software because it's easier to update, theenergy consumer has easier access to it, and it provides an aggregatesystem view.

At block 4735, a list of solutions provided by the particular entity isbuilt for the specific event or issue (or type of event or issue). Atblock 4740, a report is generated and provided to customers, sales teamsassociated with the particular entity or other appropriaterepresentatives of the entity. In embodiments, the report may includeinformation from any discrete metering device (or as a system)including: recovery time, impact on power, I/O status changes, time ofevent/time of recovery, changes in voltages/currents, changes in phasebalance, processes and/or areas impacted, etc. Report may includeinformation on SE solution (e.g., customer facing literature, featuresand benefits, technical specifications, cost, etc.), approximatesolution size required for given event (or event type), comparisons toexternal standards, placement, etc. Electrical and/or metering systemhierarchy and/or other metadata (e.g., load characteristics, etc.) maybe used to assist evaluation.

At block 4745, the report (and other information associated with themethod 4700) may be stored. In some embodiments, the report may bestored locally, for example, on a same site as the IED (or on the IEDdevice itself). Additionally, in some embodiments the report may bestored remotely, for example, in the cloud. In embodiment, blocks 4740and 4745 may be performed substantially simultaneously.

Returning now to block 4730, if it is determined that the particularentity does not provide a mitigative solution for the specified event,the method proceeds to a block 4750. At block 4750, event parametersand/or characteristics (and other information associated with the method4700) may be stored (e.g., locally and/or in the cloud). At block 4755,a report is generated based, at least in part, on select informationstored at block 4750. In embodiments, the report may include anevaluation of energy consumer impacts and needs for potential futuresolution development, third-party solutions, etc. After block 4755 (orblocks 4740/4745), the method 4700 may end.

Referring to FIG. 48, a flowchart illustrates an example method 4800 fordynamic tolerance curve generation for power quality. Similar to methods4500, 4600 and 4700 described above, in embodiments method 4800 can beimplemented on a processor of an IED and/or a processor of a controlsystem. Method 4800 may also be implemented remote from the IED in agateway, cloud, on-site software, etc. In embodiments, by evaluatingpre-event/event/post-event power characteristics of power qualityevents, it is possible (over time) to automatically develop a customevent tolerance curve for substantially any given energy consumer. Thisis extremely useful to help energy consumers identify, characterize,analyze and/or desensitize their system to power quality events.

As illustrated in FIG. 48, the method 4800 begins at block 4805, wherevoltage and/or current signals (or waveforms) are measured and capturedby an IED.

At block 4810, the voltage and/or current signals are processed toidentify a power quality event associated with one or more loadsmonitored by the IED. In some embodiments, pre-event, event andpost-event logged data may also be used to identify the power qualityevent. The pre-event, event and post-event logged data may, for example,be stored on a memory device associated with the IED and/or gateway,cloud and/or on-site software application.

At block 4815, pre-event parameters are determined from the voltageand/or current signals. In embodiments, the pre-event parameterscorrespond to substantially any parameters that can be directly measuredand/or derived from voltage and current including, but not limited to,power, energy, harmonics, power factor, frequency, event parameters(e.g., time of disturbance, magnitude of disturbance, etc.), etc. Inembodiments, pre-event data can also be derived from “statisticalnorms.” Metadata may also be used to help derive additional parametersaccordingly.

At block 4820, an impact of the power quality event is determined. Inembodiments, the event impact is calculated based on pre-event vs.post-event parameters. In embodiments, this includes both thecharacteristics of the event (i.e., magnitude, duration, disturbancetype, etc.) and its impact to load(s), process(es), system(s),facility(ies), etc. at the metered point in the system.

At block 4825, disturbance thresholds (or conditions) are compared tothe determined impact of the event. In embodiments, the disturbancethresholds may correspond to a percent change between pre-event andpost-event conditions to be considered a “significant” system,sub-system, process, and/or load disturbance. For example, a 5%reduction in load due to an electrical (or other) event may beconsidered “significant.” In embodiments, the disturbance thresholds areconfigured (e.g., pre-configured) disturbance thresholds that are storedon a memory device associated with the IED and/or gateway, cloud and/oron-site software application.

At block 4830, the IED determines if the system, sub-system, process,facility and/or load experienced (or is experiencing) a “significant”disturbance (e.g., based on the comparison at block 4825). If the IEDdetermines that the system, sub-system, process, facility and/or load(s)experienced a “significant” disturbance, the method proceeds to a block4835. Alternatively, if the IED determines that the system, sub-system,process, facility and/or load(s) has not experienced a “significant”disturbance, the method proceeds to a block 4840.

At block 4835, a disturbance point is generated and plotted asperturbative (e.g., impacting the system, sub-system, process, facilityand/or load(s), for example). At block 4845, a baseline tolerance curve(e.g., SEMI-F47, ITIC, CBEMA, etc.) is modified, changed and/orcustomized) based on characteristics associated with the specificrecorded disturbance (here, at block 4835).

Alternatively, at block 4840, in response to the IED determining thatthe system, sub-system, process, facility and/or load has notexperienced a “significant” disturbance, a disturbance point isgenerated and plotted as non-perturbative (e.g., not impacting thesystem, sub-system, process, facility and/or load(s), for example). Atblock 4845, the baseline tolerance curve is modified, changed and/orcustomized based on the characteristics associated with specificrecorded disturbance (here, at block 4840). For example, lines in thecurve may be moved between “no interruption region” and “nodamage/prohibited region.” Alternatively, the lines in the curve may notbe moved at all.

At block 4850, a report is generated. In embodiments, the report mayinclude information from substantially any discrete IED (or as a system)including: recovery time, impact on power, I/O status changes, time ofevent/time of recovery, changes in voltages/currents, imbalance changes,areas and loads impacted, etc. The report may include updated graphs oftolerance curve(s), highlighted changes in curve(s), recommendedmitigation solution(s), etc.

At block 4855, which is optional in some embodiments, at least one alarmsetting may be updated at discrete metering point(s) to match the newtolerance curve (e.g., generated at block 4845). At block 4860, the newtolerance curve (and other information associated with the method 4800)may be stored (e.g., locally, in a gateway, on-site software, and/or inthe cloud). In some embodiments, two or more of blocks 4850, 4855 and4860 may be performed substantially simultaneously. After blocks 4850,4855, and 4860, the method 4800 may end.

In general, equipment (e.g., a load or other electrical infrastructure)is designed to have a rated voltage and recommended operational range,as illustrated in FIGS. 49 and 50. The rated voltage is the desiredvoltage magnitude/level for optimal equipment operation. Additionally,the recommended operational range is the area surrounding the ratedvoltage (above and below the rated voltage) where the equipment maystill successfully operate continuously, although not necessarilyoptimally (e.g., lower efficiency, additional heating, higher currents,etc.). IED voltage event alarm thresholds (also referred to herein as“alarm thresholds” for simplicity) are typically configured (but notalways) to align with the recommended operational range so thatexcursions beyond the recommended operational range may be measured,captured and stored. This is because a strong correlation exists withexcessive voltage excursions and temporary or permanent damage to theequipment experiencing these excursions. Additionally, voltageexcursions may lead to operational issues, interruptions, loss of data,and/or any other number of impacts to equipment, processes, and/oroperations.

While the “recommended operational range” of loads, processes, and/orsystems is typically associated with a voltage magnitude, the durationof these excursions is also an important consideration. For example, a1-millisecond voltage excursion of +10% outside of the recommendedoperational range may not adversely impact the operation of a load,process, and/or system, nor impact its expected operational life.Alternatively, a 20-millisecond voltage excursion of +10% outside of therecommended operational range may cause the same load, process, and/orsystem to experience an interruption and/or reduce its life expectancy(by some extent).

FIGS. 49 and 50 illustrate two representations of the same concept.Namely, FIG. 49 shows a rms waveform and FIG. 50 shows an instantaneouswaveform. The rms waveform shown in FIG. 49 is derived from theinstantaneous waveform data shown in FIG. 50 using a well-known equation(root-mean-square) calculation. Both waveform representations are usefulfor analyzing power and energy-related issues and troubleshooting powerquality problems. Each respective graphic illustrates an exemplaryvoltage rating, upper alarm threshold, and lower alarm threshold for atheoretical load, process and/or system. In this case, the recommendedoperational range (shaded area) is assumed to align with the bounds ofthe upper and lower alarm thresholds, respectively.

Referring to FIG. 51, a flowchart illustrates an example method 5100 formanaging voltage event alarms in an electrical system that can beimplemented, for example, on a processor of at least one IED (e.g., 121,shown in FIG. 1A). Method 5100 may also be implemented remote from theat least one IED in a gateway, cloud, on-site software, remote software,etc. in some embodiments.

As illustrated in FIG. 51, the method 5100 begins at block 5105, whereenergy-related signals (or waveforms) are measured and data is captured,collected, stored, etc. by at least one IED of a plurality of IEDs in anelectrical system. In some embodiments, the energy-related signalscaptured by the at least one IED are or include at least one of:voltage, current, energy, active power, apparent power, reactive power,harmonic voltages, harmonic currents, total voltage harmonic distortion,total current harmonic distortion, harmonic power, individual phasecurrents, three-phase currents, phase voltages, and line voltages. Insome embodiments, the at least one IED is coupled to one or more loads(e.g., 111, shown in FIG. 1A) and the energy-related signals areassociated with the loads.

At block 5110, electrical measurement data from the energy-relatedsignals is processed to identify an anomalous voltage condition, forexample, at a point of installation (or metering point) of a respectiveone of the plurality of IEDs (e.g., the at least one IED) in theelectrical system. In embodiments, the anomalous voltage conditioncorresponds to a measured IED voltage, e.g., from the signals measuredat block 5105, being above or greater than one or more upper alarmthresholds or below or less than one or more lower alarm thresholds(e.g., as shown in FIGS. 49 and 50). In some embodiments, the measuredIED voltage has a magnitude and duration (rather than just a magnitude,for example). In these embodiments, the anomalous voltage condition maycorrespond to the magnitude and duration of the measured IED voltagebeing above a magnitude and duration of the one or more upper alarmthresholds, or below a magnitude and duration of the one or more loweralarm thresholds.

In some embodiments, the upper alarm thresholds and the lower alarmthresholds align with a recommended operational range of one or moreloads, processes, and/or systems monitored by the respective one of theplurality of IEDs in the electrical system. As noted above in connectionwith FIGS. 49 and 50, for example, equipment (e.g., loads) may have arated voltage and a recommended operational range, with the ratedvoltage corresponding to a desired voltage magnitude/level for optimalload operation, and the recommended operational range being the areaabove or below the rated voltage where the loads may still operatecontinuously, although not necessarily optimally.

In some embodiments, the upper alarm thresholds and the lower alarmthresholds are also indicated in a dynamic tolerance curve associatedwith one or more loads monitored by the respective one of the pluralityof IEDs in the electrical system, for example, as described inconnection with FIGS. 34-40. In embodiments, the dynamic tolerance curvecharacterizes the response of the one or more loads to power qualityevents. Additionally, in embodiments the dynamic tolerance curvecharacterizes an impact (or affect) of the at least one power qualityevents on the electrical system at the point where the IED capturing theat least one power quality event is physically installed in theelectrical system. The dynamic tolerance curve may include a magnitudethreshold on the high side of the nominal voltage, a duration thresholdon the high side of the nominal voltage, a magnitude threshold on thelow side of the nominal voltage, and a duration threshold on the lowside of the nominal voltage, for example.

In some embodiments, the anomalous voltage condition (e.g., voltageperturbation) is indicative of a voltage event, which is one exampletype of power quality event, as discussed above. In embodiments, thevoltage event may be characterized based on just the magnitude or themagnitude and duration of the measured IED voltage. The voltage eventmay include, for example, one of a voltage sag, a voltage swell, avoltage transient, an instantaneous interruption, a momentaryinterruption, a temporary interruption, and a long-durationroot-mean-square (rms) variation. In embodiments, the voltage event maybe characterized based on the magnitude and duration values in the tableof categories and characteristics of power system electromagneticphenomena from IEEE Standard 1159-2019 (known art), for example, as setforth above in the summary section of this disclosure. In someembodiments, the anomalous voltage condition identified at block 5110corresponds to an initial condition that may change, for example, as thedynamic tolerance curve is built for the discrete point where the IEDmeasuring the data is installed.

At block 5115, it is determined if the electrical system is affected bythe identified anomalous voltage condition. Example methods fordetermining if the electrical system is affected by the identifiedanomalous voltage condition are discussed below in connection with FIGS.52-53. However, let it suffice here to say that in some embodimentsimpacts (or affects) of the identified anomalous voltage condition onthe electrical system may be determined by identifying loss of load inthe electrical system, and/or by comparing pre- and post-event values ofparameters associated with the electrical system.

If it is determined that the electrical system is affected by theidentified anomalous voltage condition, the method may proceed to block5120 in some embodiments. In other embodiments, the method may end.Alternatively, if it is determined that the electrical system is notaffected by the identified anomalous voltage condition, the method mayproceed to block 5130.

In some embodiments, in response to determining that the electricalsystem is affected by the identified anomalous voltage condition,voltage data associated with the identified anomalous voltage conditionmay be stored in a memory device associated with the at least one IED(e.g., 215, shown in FIG. 1B) and the identified anomalous voltagecondition may be classified as “impactful” to the electrical system.Additionally, in some embodiments in response to determining that theelectrical system is not affected by the identified anomalous voltagecondition, voltage data associated with the identified anomalous voltagecondition may be stored in the memory device associated with the atleast one IED and the identified anomalous voltage condition may beclassified as “non-impactful” to the electrical system.

An impactful event may, for example, correspond to an event thatinterrupts operation (or effectiveness) of the loads, processes and/orthe electrical system including the loads. This, in turn, may impact anoutput of the system, for example, the production, quality, rate, etc.of a product generated by the system. In some embodiments, the productmay be a physical/tangible object (e.g., a widget). Additionally, insome embodiments the product may be a non-physical object (e.g., data orinformation). The impactful event may also adversely impact theequipment manufacturing the product generated by the system in someembodiments. A non-impactful event, by contrast, may correspond to anevent that does not interrupt (or minimally interrupts) operation (oreffectiveness) of the loads, processes, and/or the electrical systemincluding the loads. Discrete impactful and/or non-impactful events maybe indicated in dynamic tolerance curves according to the disclosure, asshown in FIGS. 7 and 8, for example.

At block 5120, which is optional in some embodiments, it is determinedif an action (or actions) is/are required in response to the anomalousvoltage condition. If it is determined that an action (or actions)is/are required in response to the anomalous voltage condition, themethod may proceed to block 5125. Alternatively, if it is determinedthat an action (or actions) is/are not required in response to theanomalous voltage condition, the method may end in some embodiments. Inother embodiments, the method may return to block 5105 and repeat again.

At block 5125, which is also optional in some embodiments, an action (oractions) may be performed in response to the anomalous voltagecondition. More particularly, is embodiments at least one source of theanomalous voltage condition is identified at block 5125, and one or moreactions are performed based on the identified source. In embodiments,the actions are performed based on a type (or types) of the identifiedsource and/or a location (or locations) of the identified source, forexample. The location(s) of the identified source may, for example, beupstream or downstream relative to a physical or virtual metering pointin the electrical system associated with the at least one IED capturingthe energy-related signals (e.g., voltage and/or current signals) atblock 5105. In embodiments, the identified source may be on a utilityside of the at least one IED, or on the customer side of the at leastone IED, for example.

In some embodiments, the metering point is associated with a respectivezone or portion of the electrical system. In embodiments, the at leastone IED may be capable of determining if one or more zones or portionsother than the respective zone or portion are affected by the anomalousvoltage condition, for example, by communicating with (e.g., sharingdata obtained from captured energy-related signals at block 5105 with)IEDs in the other zones or portions. IEDs and their associated zones aredescribed more above in connection with FIGS. 29-33, for example. Insome embodiments, the aforementioned is typically performed at a higherlevel, for example, where the cloud, software and/or gateway are viewingmultiple IEDs.

Typical causes of voltage sags, for example, which are one example typeof voltage event that an anomalous voltage condition may be indicativeof, are faults and loads turning on or off. This includes operation ofprotective equipment to clear faults from a circuit (e.g., breakers,fuses, reclosers, etc.) to which the at least one IED is coupled (orinstalled). Other sources of anomalous voltage conditions (and voltageevents that the anomalous voltage conditions may be indicative of)include lightning strikes, capacitor banks energizing, equipmentoperation and misoperation, equipment failures, switching events,squirrels in lines, construction dig-ins, trees in lines, etc.

Example actions that may be performed at block 5125 in response to theanomalous voltage condition may include, for example, reporting theanomalous voltage condition (e.g., through a voltage event alarmgenerated by the at least one IED) and/or automatically performing anaction (or actions) affecting at least one component of the electricalsystem, e.g., starting a diesel generator, operating a throw-over orstatic switch, etc. Similar to method 4500 described above in connectionwith FIG. 45, in some embodiments a control signal may be generated inresponse to the anomalous voltage condition, and the control signal maybe used to affect the at least one component of the electrical system(e.g., a load monitored by the at least one IED, or a mitigative devicesuch as a diesel generator, throw-over or static switch, etc.). Thecontrol signal may be generated by the at least one IED, a controlsystem, or another device, software or system associated with theelectrical system. As discussed in figures above, in some embodimentsthe at least one IED may include or correspond to the control system.Additionally, in some embodiments the control system may include the atleast one IED.

As another example, an action that may be performed at block 5125 isstarting and stopping a timer to quantify a length (or duration) of theimpact to production, for example, in a facility with which the impactis associated. This will help a user make better decisions regardingoperation of the facility during atypical conditions.

Returning now to block 5115, in response to it having been determinedthat the electrical system is not affected by the identified anomalousvoltage condition, the method may proceed with block 5130. At block5130, at least one of the upper alarm thresholds or at least one of thelower alarm thresholds is adjusted to the measured IED voltage. Inembodiments, the at least one of the upper alarm thresholds or the atleast one of the lower alarm thresholds is adjusted to the measured IEDvoltage to more accurately represent true voltage event sensitivity atthe respective one of the plurality of IEDs point of installation in theelectrical system. In some embodiments, the at least one of the upperalarm thresholds or the at least one of the lower alarm thresholds isadjusted to a magnitude and duration of the measured IED voltage, forexample, in embodiments in which the alarm thresholds and measured IEDvoltage both have an associated magnitude and duration, as noted abovein the description of block 5110.

Subsequent to block 5130 (or block 5125), the method 5100 may end insome embodiments. In other embodiments, the method 5100 may repeatagain, for example, in response to a control signal or user input, orautomatically to ensure that additional anomalous voltage conditions arecaptured.

Referring to FIG. 52, a flowchart illustrates an example method 5200 fordetermining if an electrical system is affected by an anomalous voltagecondition. The method 5200 may be implemented, for example, on aprocessor of at least one IED (e.g., 121, shown in FIG. 1A) and/orremote from the at least one IED in a gateway, cloud, on-site software,etc. In embodiments, the method 5200 may correspond to example stepsperformed at block 5115 of method 5100 discussed above in connectionwith FIG. 51.

As illustrated in FIG. 52, the method 5200 begins at block 5205, whereloss of load is identified in an electrical system associated with ananomalous voltage condition (e.g., as identified at block 5110, shown inFIG. 51). As noted above in connection with block 5105 of method 5100,at least one IED may be coupled to one or more loads (e.g., 111, shownin FIG. 1A) in an electrical system and configured to monitor the loads.

In embodiments, the loss of load is identified by comparing one or morepre-event parameters associated with the loads to one or more post-eventparameters associated with the loads. The loss of load may correspond,for example, to one or more of the loads operating at a capacity orcapability that is less than optimal or not operating at all, e.g., asdetermined by comparing the pre-event parameters to the post-eventparameters. In embodiments, the pre-event parameters and the post-eventparameters are determined or calculated from electrical measurement datafrom energy-related signals captured by at least one IED coupled to theloads (e.g., at block 5105 of method 5100). Examples of pre- andpost-event parameters include, for example, power, current, voltageharmonic distortion, power factor, imbalance, etc. The loss of load maybe indicative of the electrical system being affected by an anomalousvoltage condition. The loss of load may be presented as an absolutevalue, percent of average, percent of rated, or other measurementmetric, for example.

In some embodiments, the loss of load may be indicative of one or moreloads having experienced an excursion (e.g., a voltage excursion)outside of a recommended operational range of the loads. As noted abovein connection with FIGS. 49 and 50, for example, equipment (e.g., loads)typically have a rated voltage and a recommended operational range. Anexcursion outside of the recommended voltage may or may not impact loads(i.e., a loss of load), for example, based on operationalcharacteristics of the loads (e.g., response to changes in voltageoutside of recommended operational range) and a duration of theexcursion. As noted above, the duration of the excursion may be animportant consideration. For example, a 1-millisecond voltage excursionof +10% outside of the recommended operational range may not adverselyimpact the operation of a load, process, and/or system, nor impact itsexpected operational life. Alternatively, a 20-millisecond voltageexcursion of +10% outside of the recommended operational range may causethe same load, process, and/or system to experience an interruptionand/or reduce its life expectancy (by some degree).

At block 5210, a load loss alarm indicating the identified loss of loadis generated and/or initiated, for example, on the at least one IED. Inembodiments, the load loss alarm indicates at least one of a location ofthe identified loss of load, a type of the identified loss of load, anda severity or level of the identified loss of load. In some embodiments,the load loss alarm may further indicate at least one of: change inpower, change in energy, change in phase balance, change in voltage,change in power factor, change in harmonic distortion, change incurrent, change in any other measured and/or derived parameter, andchanges in digital and/or analog inputs and outputs.

At block 5215, which is optional in some embodiments, the load lossalarm is communicated via at least one of: a report, a text, an email,audibly, and an interface of a screen/display. In some embodiments, thescreen/display is a screen/display of a user device (e.g., of a userresponsible for monitoring a facility including the electrical system).In some embodiments, the load loss alarm may be communicated, indicated,stored, analyzed, managed and/or utilized in at least one or morecomponent (e.g., IED, gateway) and/or system (e.g., on-site,cloud-based) associated with the electrical system, control system,and/or power monitoring system. It is understood that the load lossalarm may be communicated by some other interactive means. It is alsounderstood that the load loss alarm may be communicated to a controlsystem in some embodiments, for example, for controlling one or moreparameters or equipment associated with the electrical system inresponse to the load loss alarm. As noted above, in some embodiments theat least one IED may include or correspond to the control system.Additionally, in some embodiments the control system may include the atleast one IED. Example actions that may be taken by the control systemmay include, for example, opening or closing breakers, initiating orturning off equipment, staring a diesel generator (or some otheralternate energy source), etc. to pick up the load, sending personnel tothe load to restart the equipment, etc.

Subsequent to block 5215, the method 5200 may end in some embodiments.

Referring to FIG. 53, a flowchart illustrates another example method5300 for determining if an electrical system is affected by an anomalousvoltage condition. The method 5300 may be implemented, for example, on aprocessor of at least one IED (e.g., 121, shown in FIG. 1A) and/orremote from the at least one IED in a gateway, cloud, on-site software,etc. In embodiments, the method 5300 may correspond to example stepsperformed at block 5115 of method 5100 discussed above in connectionwith FIG. 51.

As illustrated in FIG. 53, the method 5300 begins at block 5305, whereone or more first parameters associated with the electrical system aremeasured or calculated, for example, from energy-related signals (orwaveforms) captured by at least one IED at a first time. Additionally,at block 5310 one or more second parameters associated with theelectrical system are measured or calculated, for example, fromenergy-related signals (or waveforms) captured by the at least one IEDat a second time.

In some embodiments, the first parameters correspond to parametersassociated with historical measurement data from before the anomalousvoltage condition or event is detected or identified, and the first timecorresponds to a time before the anomalous voltage condition isidentified. Additionally, in some embodiments the second parameterscorrespond to parameters associated with measurement data from after theanomalous voltage condition is identified, and the second timecorresponds to a time after the anomalous voltage condition isidentified.

In some embodiments, the first parameters are substantially the same asthe second parameters. For example, in some embodiments the first andsecond parameters may both include energy-related parameters. Example ofenergy-related parameters may include at least one of voltage, current,energy, real power, apparent power, reactive power, harmonic voltages,harmonic currents, total voltage harmonic distortion, total currentharmonic distortion, harmonic power, individual phase currents,three-phase currents, phase voltages, and line voltages.

At block 5315, the first parameters (e.g., pre-event parameters) arecompared with the second parameters (e.g., post-event parameters) todetermine if the electrical system is affected by the identifiedanomalous voltage condition.

In some embodiments, comparing the first parameters to the secondparameters to determine if the electrical system is affected by theidentified anomalous voltage condition includes comparing the firstparameters to the second parameters to determine if a meaningfuldiscrepancy exists between the first parameters and the secondparameters. In embodiments, in response to determining that a meaningfuldiscrepancy exists between the first parameters and the secondparameters, it may be determined that the electrical system is affectedby the identified anomalous voltage condition. Additionally, inembodiments in response to determining that a meaningful discrepancydoes not exist between the first parameters and the second parameters,it may be determined that the electrical system is not affected by theidentified anomalous voltage condition.

In some embodiments, the meaningful discrepancy refers to a change thatis large enough to indicate the electrical system's operation has“changed” due to the anomalous voltage condition or event. This changemay be about 1%, about 5% or some other magnitude deemed to be relevantby those operating a facility including the electrical system. Ameaningful discrepancy may be manually configured, automaticallylearned, or suggested based on, for example, historical system dataand/or specific market segment types (e.g., hospital, data center,wastewater treatment facility, etc.).

Subsequent to block 5315, the method 5300 may end in some embodiments.

Referring to FIG. 54, a flowchart illustrates an example method 5400 forprioritizing voltage event alarms. The method 5400 may be implemented,for example, on a processor of at least one IED (e.g., 121, shown inFIG. 1A) and/or remote from the at least one IED in a gateway, cloud,on-site software, etc. In embodiments, the method 5400 may correspond toexample steps performed at block 5125 of method 5100 discussed above inconnection with FIG. 51.

As illustrated in FIG. 54, the method 5400 begins at block 5405, whereone or more new voltage event alarms are received, for example, at aninput of a processor of at least one IED. In embodiments, the voltageevent alarms may be generated and/or initiated in response to the leastone IED determining that an electrical system is affected by anidentified anomalous voltage condition (e.g., at block 5115 of method5100). Additionally, in embodiments the voltage event alarms may begenerated and/or initiated in response to the least one IED determiningthat an action (or actions) is/are required in response to theidentified anomalous voltage condition (e.g., at block 5120 of method5100). As discussed in connection with figures above, an anomalousvoltage condition may be indicative of a voltage event (or anotheranomalous electrical event, such as a neutral and/or grounding event) inembodiments in which the anomalous voltage condition affects theelectrical system.

At block 5410, it is determined if any other voltage event alarms existin the electrical system, for example, at a point of installation of theat least one IED in the electrical system. If it is determined thatother voltage events exist in the electrical system, the method mayproceed to block 5415. Alternatively, if it is determined that no othervoltage events exist in the electrical system, the method may proceed toblock 5425. In embodiments, it may be determined if other voltage eventsexist in the electrical system by searching a database or memory deviceassociated with the at least one IED to see if any other voltage eventshave been recorded in the electrical system.

At block 5415, the priority of the voltage event alarms may be updatedbased on the new voltage event alarms. In embodiments, the priority isupdated based on location(s) of voltage events associated with thevoltage event alarms, type of voltage events, severity of the voltageevents, etc. In some embodiments, voltage events of greater severity,longer duration, and/or greater impact may be prioritized higher.Alternatively, voltage events that impact specific systems based on userconfigurations may be prioritized higher.

At block 5425, in response to it having been determined that no othervoltage event alarms exist in the electrical system besides the newvoltage event alarms, the new voltage event alarms may be prioritized.Similar to block 5415, the priority of the new voltage event alarms maybe based on location(s) of voltage events associated with the newvoltage event alarms, type of voltage events, severity of the voltageevents, etc. Additionally, similar to block 5415, in some embodimentsvoltage events of greater severity, longer duration, and/or greaterimpact may be prioritized higher. Alternatively, voltage events thatimpact specific systems based on user configurations may be prioritizedhigher.

At block 5420, one or more actions may be performed based on thepriority of the voltage event alarms, e.g., as determined at block 5415or 5425. Similar to block 5125 of method 5100 discussed above, exampleactions that may be performed at block 5420 may include, for example,reporting the anomalous voltage condition or voltage event (e.g.,through the voltage event alarm generated and/or initiated by the atleast one IED) and/or automatically performing an action (or actions)affecting at least one component of the electrical system.

Subsequent to block 5420, the method 5400 may end in some embodiments.In other embodiments, the method 5400 may repeat again, for example, inresponse to a control signal or user input, or automatically such thatthe priority of the voltage event alarms is continuously,semi-continuously or periodically evaluated, or evaluated based ondemand (e.g., from a user).

Referring to FIG. 55, a flowchart illustrates another example method5500 for managing voltage event alarms in an electrical system that canbe implemented, for example, on a processor of at least one IED (e.g.,121, shown in FIG. 1A). Method 5500 may also be implemented remote fromthe at least one IED in a gateway, cloud, on-site software, remotesoftware, a control system or device associated with the at least oneIED, etc. in some embodiments.

As illustrated in FIG. 55, the method 5500 begins at block 5505, whereenergy-related signals (or waveforms) are measured and data is captured,collected, stored, etc. by at least one IED of a plurality of IEDs in anelectrical system. In some embodiments, the energy-related signalscaptured by the at least one IED are or include at least one of avoltage signal, a current signal, analog input/output (I/O) data,digital I/O data, and a derived or extracted value. Examples of each ofthese example types of signals/data are noted in the Summary Section ofthis disclosure. In some embodiments, the at least one IED is directlyor indirectly coupled to one or more loads (e.g., 111, shown in FIG. 1A)and the energy-related signals are associated with the loads.

At block 5510, electrical measurement data from, or derived from, theenergy-related signals is processed to identify an anomalous voltagecondition, for example, at a point of installation (or metering point)of a respective one of the plurality of IEDs (e.g., the at least oneIED) in the electrical system. As noted above in connection with earlierfigures, in some embodiments the anomalous voltage condition correspondsto a measured IED voltage, e.g., from the signals measured at block5505, being above one or more upper alarm thresholds or below one ormore lower alarm thresholds (e.g., as shown in FIGS. 49 and 50). As alsonoted above in connection with earlier figures, in some embodiments themeasured IED voltage has a magnitude and duration (rather than just amagnitude, for example). In these embodiments, the anomalous voltagecondition may correspond to the magnitude and duration of the measuredIED voltage being above or greater than a magnitude and duration of theone or more upper alarm thresholds, or below or less than a magnitudeand duration of the one or more lower alarm thresholds.

In some embodiments, the upper alarm thresholds and the lower alarmthresholds align with a recommended operational range of one or moreloads, processes, and/or systems monitored by the respective one of theplurality of IEDs in the electrical system. As noted above in connectionwith FIGS. 49 and 50 and other earlier figures, equipment (e.g., loads)may have a rated voltage and a recommended operational range, with therated voltage corresponding to a desired voltage magnitude/level foroptimal load operation, and the recommended operational range being thearea above or below the rated voltage where the loads may still operatecontinuously, although not necessarily optimally.

In some embodiments, the upper alarm thresholds and the lower alarmthresholds are also indicated in a dynamic tolerance curve associatedwith one or more loads monitored by the respective one of the pluralityof IEDs in the electrical system, for example, as described inconnection with FIGS. 34-40. In embodiments, the dynamic tolerance curvecharacterizes the response of the one or more loads to power qualityevents. Additionally, in embodiments the dynamic tolerance curvecharacterizes an impact (or affect) of the at least one power qualityevents on the electrical system at the point where the IED capturing theat least one power quality event is physically installed in theelectrical system. The dynamic tolerance curve may include a magnitudethreshold (y-axis) on the high side of the nominal voltage, a durationthreshold on the high side (x-axis) of the nominal voltage, a magnitudethreshold (again, y-axis) on the low side of the nominal voltage, and aduration threshold on the low side (again, x-axis) of the nominalvoltage, for example.

In some embodiments, the anomalous voltage condition (e.g., voltageperturbation, etc.) is indicative of a voltage event, which is oneexample type of power quality event, as discussed above. In embodiments,the voltage event may be characterized based on just the magnitude orthe magnitude and duration of the measured IED voltage. The voltageevent may include, for example, one of a voltage sag, a voltage swell, avoltage transient, an instantaneous interruption, a momentaryinterruption, a temporary interruption, and a long-durationroot-mean-square (rms) variation. In embodiments, the voltage event maybe characterized based on a combination of the magnitude and durationvalues in the table of categories and characteristics of power systemelectromagnetic phenomena from IEEE Standard 1159-2019 (known art), forexample, as set forth above in the Summary Section of this disclosure.In some embodiments, the anomalous voltage condition identified at block5510 corresponds to an initial condition that may change, for example,as the dynamic tolerance curve is built for the discrete point where theIED measuring/capturing/logging the data is installed.

At block 5515, it is determined if the electrical system is affected bythe identified anomalous voltage condition. In accordance with someembodiments of this disclosure, the determination is based, at least inpart, on an evaluation of at least one of a magnitude and a duration ofthe identified anomalous voltage condition. In accordance with someembodiments of this disclosure, the at least one of the magnitude andthe duration of the identified anomalous voltage condition used in theevaluation corresponds to a measured magnitude and a measured durationof the identified anomalous voltage condition. Additionally, inaccordance with some embodiments of this disclosure, at least one of themeasured magnitude and the measured duration of the identified anomalousvoltage condition is compared to at least one of a reference magnitudeand a reference duration to determine if the electrical system isaffected by the identified anomalous voltage condition. The referencemagnitude and the reference duration may be used, in part, to determineimpact of the identified anomalous voltage condition on the electricalsystem, for example. It is understood that additional example means fordetermining if the electrical system is affected by the identifiedanomalous voltage condition are discussed above in connection with FIGS.52-53, for example.

If it is determined that the electrical system is affected by theidentified anomalous voltage condition, the method may proceed to block5520 in some embodiments. Alternatively, if it is determined that theelectrical system is not affected by the identified anomalous voltagecondition, the method may proceed end in some embodiments.

In some embodiments, in response to determining that the electricalsystem is affected by the identified anomalous voltage condition,voltage data associated with the identified anomalous voltage conditionmay be stored in a memory device associated with the at least one IED(e.g., 215, shown in FIG. 1B) and the identified anomalous voltagecondition may be classified as “impactful” to the electrical system.Additionally, in some embodiments in response to determining that theelectrical system is not affected by the identified anomalous voltagecondition, voltage data associated with the identified anomalous voltagecondition may be stored in the memory device associated with the atleast one IED and the identified anomalous voltage condition may beclassified as “non-impactful” to the electrical system.

As discussed above in connection with FIG. 51, for example, an impactfulevent may, for example, correspond to an event that interrupts operation(or effectiveness) of the loads, processes and/or the electrical systemincluding the loads. This, in turn, may impact an output of the system,for example, the production, quality, rate, etc. of a product generatedby the system. In some embodiments, the product may be aphysical/tangible object (e.g., a widget). Additionally, in someembodiments the product may be a non-physical object (e.g., data orinformation). The impactful event may also adversely impact theequipment manufacturing the product generated by the system in someembodiments. A non-impactful event, by contrast, may correspond to anevent that does not interrupt (or minimally interrupts) operation (oreffectiveness) of the loads, processes, and/or the electrical systemincluding the loads. Discrete impactful and/or non-impactful events maybe graphically indicated in dynamic tolerance curves and/or tabularlyindicated in tables, etc. according to the disclosure, as shown in FIGS.7 and 8, for example.

At block 5520, at least one of a plurality of criteria are chosen toadjust at least one of the upper alarm thresholds and/or at least one ofthe lower alarm thresholds. More detailed embodiments associated withchoosing the at least one of the plurality of criteria are discussedbelow in connection with FIG. 56, for example. However, let it sufficehere to say that in some embodiments the at least one of the pluralityof criteria are chosen based on at least one of information associatedwith the anomalous voltage condition (e.g., characteristics of theanomalous voltage condition) and information relating to the electricalsystem installation (e.g., acceptable recovery time for customer site,customer segment, etc.).

At block 5525, at least one of the upper alarm thresholds and/or atleast one of the lower alarm thresholds related to at least one of themagnitude and duration is adjusted based on the at least one of theplurality of criteria.

Subsequent to block 5525, the method 5500 may end in some embodiments.In other embodiments, the method 5500 may repeat again, for example, inresponse to a control signal or user input, or automatically to ensurethat additional anomalous voltage conditions are captured. The controlsignal may be generated, for example, in response to an updated statusand/or condition with respect to a load, loads, process, processes, etc.associated with the electrical system.

It is understood that method 5500 may include one or more additionalblocks or steps in some embodiments. For example, in some embodimentsthe method 5500 may further include determining if an action (oractions) is/are required in response to the anomalous voltage condition,and performing or taking the action(s) in response to determining theaction(s) are required, similar to blocks 5120 and 5125 discussed abovein connection with FIG. 51. Additionally, in some embodiments the method5500 may further include validation of the adjust threshold(s), andstoring of information relating to the adjusted threshold(s), asdiscussed in connection with FIG. 56. It is understood that method 5500may include many other additional or alternative blocks or steps in someembodiments, as will be understood by one of ordinary skill in the art.

Referring now to FIG. 56, a flowchart illustrates an example method 5600for adjusting and validating voltage event alarm threshold(s). Similarto method 5500, method 5600 may be implemented, for example, on aprocessor of at least one IED (e.g., 121, shown in FIG. 1) and/or remotefrom the at least IED, for example, in at least one of: a cloud-basedsystem, on-site software, a gateway, or another head-end system. Inaccordance with some embodiments of this disclosure, method 5600corresponds to example steps performed at blocks 5520 and 5525 of method5550.

As illustrated in FIG. 56, the method 5600 begins at block 5605, whereinformation associated with an anomalous voltage condition, for example,the anomalous voltage condition identified at block 5510 of method 5500,is received. The information may include, for example, one or morecharacteristics of the anomalous voltage condition. In accordance withsome embodiments of this disclosure, the one or more characteristics ofthe anomalous voltage condition include at least one of: anomalousvoltage condition type (e.g., power quality event type), impact of theanomalous voltage condition on the electrical system (e.g., as may bedetermined at block 5515 of method 5500), time for the electrical systemto recover from the anomalous voltage condition (i.e., recovery time),the response of one or more load types to the anomalous voltagecondition, time(s) of occurrence of the anomalous voltage condition, andhistorical data associated with the anomalous voltage condition.

In accordance with some embodiments of this disclosure, informationrelating to the impact of the anomalous voltage condition on theelectrical system may indicate, for example, a particular amount of loadimpacted, either by absolute value (e.g., 10 kW) or relative percentageof nominal (e.g., 10% of the average load). Additionally, theinformation relating to the impact of the anomalous voltage conditionmay indicate a particular zone, process and/or IED impacted, aparticular level of production losses, a particular location of theanomalous voltage condition (upstream/upline or downstream/downline), aparticular cost (e.g., manufacturing, business, or intangible)associated with the anomalous voltage condition, time of occurrence(time of day, day of week, week of year, month of year, season of year,etc.), and mitigated vs. not mitigated events, as a few examples.

In accordance with some embodiments of this disclosure, the recoverytime is associated with impact of the anomalous voltage condition on theelectrical system, for example. Referring briefly to FIG. 57, shown isan example dynamic tolerance curve illustrating various characteristicsassociated with anomalous voltage conditions, for example, impact andrecovery time associated with impactful voltage events. The areaindicated by reference designator 5705 in this example dynamic tolerancecurve is the “impact” zone for voltage events meaning an impact isexpected from a voltage event having corresponding magnitude/durationwithin this area. The “impact” zone is delineated from the no-impactzone with a solid black line. The combined shaded/dark andnon-shaded/light areas shown in this dynamic tolerance curve representthe “no-impact” zone for voltage events. The “no-impact” zone isdelineated from the shaded/dark area with a dashed line. The shaded/darkarea shown in this dynamic tolerance curve is a subset of the“no-impact” zone, and have additional limitations on impactful voltageevents beyond merely being impactful. For example, events in theshaded/dark area of this dynamic tolerance curve illustrate impactfulvoltage events which result in at least 5 minutes of recovery time.

In accordance with some embodiments of this disclosure, the recoverytime corresponds to accumulated recovery time, the accumulated recoverytime associated with recurrence of the anomalous voltage condition overone or more periods of time (e.g., days, weeks, etc.). For example, itis contemplated that anomalous voltage conditions may recur over one ormore periods of time and the recovery time data associated with theanomalous voltage condition (i.e., over the one or more periods of time)may be aggregated. In accordance with some embodiments of thisdisclosure, it may be acceptable, for example, for the anomalous voltagecondition to recur six times in one month if the aggregated/accumulatedrecovery time is low, but not acceptable if the aggregated recovery timeis high. In accordance with some embodiments of this disclosure, theaccumulated recovery time may be reset in response to one or moreconditions (e.g., user specified conditions). The conditions mayinclude, for example, accumulated recovery time being reset after apredetermined period of time (e.g., each week or month).

In accordance with some embodiments of this disclosure, the historicaldata associated with the anomalous voltage condition that may bereceived at block 5605 may include, for example, at least one ofnon-impactful event data and impactful event data from historicalevents. In accordance with some embodiments of this disclosure, thehistorical events may be associated with one or more aspects, portions,zones, processes, etc. (e.g., IED installation points) of or associatedwith the electrical system. The historical events may be grouped and/orarchived, for example, based on an end-user segment under consideration(i.e., an end-user segment associated with the electrical system).Examples of end-user segments may include at least one of: retail,offices, hotels, hospitals, data centers, food and beverages, oil andgas, industrial, automotive, utility, manufacturing, educational,governmental, residential and commercial, for example.

It is understood that the historical data and other types of informationassociated with an anomalous voltage condition may be stored andretrieved from at least one memory device associated with the electricalsystem in accordance with embodiments of this disclosure. The at leastone memory device may correspond, for example, to at least one memorydevice of at least one of an IED, a cloud-based system, on-sitesoftware, a gateway, or another head-end system.

At block 5610 of method 5600, information associated with the electricalsystem installation may be received. For example, the information may bereceived from one or more systems and/or devices in or associated withthe electrical system and/or from an end-user. Examples of theinformation received from the one or more systems and/or devices (e.g.,sensor devices) may include, for example, I/O data associated with theelectrical system installation. In accordance with some embodiments ofthis disclosure, the I/O data may include, for example, a digital signal(e.g., two discrete states) and/or an analog signal (e.g., continuouslyvariable). The digital signal may include, for example, at least one ofon/off status(es), open/closed status(es), high/low status(es),synchronizing pulse and any other representative bi-stable signalassociated with the electrical system installation. Additionally, theanalog signal may include, for example, at least one of temperature,pressure, volume, spatial, rate, humidity, and any other representativephysically representative signal associated with the electrical systeminstallation. In general, the information (e.g., I/O data) received fromthe one or more systems and/or devices may indicate a particular changein some parameter (i.e., electrical, mechanical, I/O, etc.).

Examples of the information received from the end-user at block 5610 mayinclude, for example, end-user segment information and/or acceptancethreshold(s) or limit(s). The end-user segment information may indicateend-user segment(s) associated with the electrical system installation(e.g., retail, offices, hotels, hospitals, etc., as noted above).Additionally, the acceptance threshold(s) or limit(s) may indicateacceptable recovery time for customer site, acceptable amount(s) ofload(s) impacted, acceptable level of production losses and other costs.

At block 5615, at least one of a plurality of criteria is chosen toadjust at least one upper alarm thresholds and/or at least one loweralarm thresholds (e.g., magnitude, duration), for example, as brieflydiscussed above in connection with FIG. 55. In accordance with someembodiments of this disclosure, the at least one of a plurality ofcriteria is chosen based on at least one of the where informationassociated with an anomalous voltage condition and the informationassociated with the electrical system installation received at blocks5605 and 5610, respectively, and/or learned information (e.g., usingmachine learning techniques). For example, in accordance with someembodiments of this disclosure, the chosen at least one of the pluralityof criteria includes at least one of recovery time, the response of oneor more load types to the anomalous voltage condition, I/O data, userconfigured criteria, time(s) of occurrence of the identified anomalousvoltage condition, and historical data.

In accordance with some embodiments of this disclosure, the at least oneof the plurality of criteria is chosen based on information learned fromcurrent input data (e.g., from systems and/or devices in the electricalsystem, and/or user inputs) and/or the historical data. An end-userand/or system operators (e.g., responsible for providing the userinputs) may teach or train the system(s) and/or device(s) that may beresponsible for generating the plurality of criteria and/or for choosingthe plurality of criteria, for example, using training data (e.g., thehistorical data). In accordance with some embodiments of thisdisclosure, the plurality of criteria are stored in and accessed from alibrary of criteria.

At block 5620, the at least one upper alarm thresholds and/or at leastone lower alarm thresholds is/are adjusted based on the at least one ofthe plurality of criteria chosen at block 5615. In accordance with someembodiments of this disclosure, the amount(s) and/or level(s) by whichthe at least one of the upper alarm thresholds and/or the at least oneof the lower alarm thresholds is/are adjusted is/are based on anevaluation of at least one characteristic associated with the identifiedanomalous voltage condition with respect to at least one of theplurality of criteria. The at least one characteristic associated withthe identified anomalous voltage condition may include, for example, atleast one of the magnitude and the duration of the identified anomalousvoltage condition.

At block 5625, it is determined if the adjusted alarm threshold(s)is/are acceptable. For example, the adjusted alarm threshold(s) may bevalidated using user-defined, prescriptive (e.g., standards) ormanufacturer recommendations, baselines, etc. In accordance with someembodiments of this disclosure, all of the aforesaid validation criteriamay supersede part or all of the threshold recommendations made by theconcepts outlined in this disclosure. If it is determined the adjustedalarm threshold(s) is/are acceptable, the method may proceed to block5630. Alternatively, if it is determined the adjusted alarm threshold(s)is/are not acceptable, the method may return to block 5615 where atleast one other criteria (or a different combination of criteria) arechosen to adjust the alarm threshold(s).

At block 5630, information relating to adjusted alarm threshold(s) maybe stored, for example, for future use. For example, the information maybe stored as data to be used as historical data in future analyses.

Subsequent to block 5630, the method 5600 may end in some embodiments.In other embodiments, the method 5600 may repeat again, for example, inresponse to a control signal, user input, or automatically. It isunderstood that method 5600 may include one or more additional blocks orsteps in some embodiments. For example, in some embodiments the method5600 may further include using the information stored at block 5630 togenerate or update a library of criteria that may be used in futureanalyses. Additionally, in some embodiments the method 5600 may furtherinclude prioritizing and/or reprioritizing alarms associated with thealarm threshold(s). Further, in some embodiments the method 5600 mayfurther include taking one or more actions, for example, depending onthe at least one of the plurality of criteria chosen and used to adjustthe at least one upper alarm thresholds and/or at least one lower alarmthresholds. For example, in embodiments in which the chosen at least oneof the plurality of criteria includes the time(s) of occurrence of theanomalous voltage condition, a voltage event alarm or alarms associatedwith the one or more upper alarm thresholds and/or the one or more loweralarm thresholds may be configured to be turned on, off, or adjusted(e.g., increased or decreased) in response to the time(s) of occurrenceof the anomalous voltage condition meeting certain criteria. The certaincriteria may include, for example, a time or times at which the voltageevent alarm or alarms are configured to be silenced or muted (e.g., fora given time period). It is understood that the above are but a few ofmany possible examples of additional or alternative steps that method5600 may include.

It is also understood that many other embodiments are contemplated inthis disclosure. For example, in accordance with some embodiments ofthis disclosure, the disclosed dynamic tolerance curves may be updated,generated or regenerated based on the above-discussed times(s) ofoccurrence of the identified anomalous voltage condition. For example, afirst dynamic tolerance curve may be generated for a first day or timeperiod, a second dynamic tolerance curve may be generated for a secondday or time period.

As illustrated above, and as will be appreciated by one of ordinaryskill in the art, embodiments of the disclosure promote “more andbetter” metering within facilities. For example, the more IEDs installedin an energy consumer's electrical system, the more beneficial theseembodiments may be for the energy consumer. As will also be appreciatedby one of ordinary skill in the art, there are significant opportunitiesfor voltage event mitigation products. Further, it will be appreciatedby one of ordinary skill in the art that it is important to identify andpromote opportunities that would have typically been overlooked,misunderstood, or simply ignored by energy consumers. The ability toquantify voltage events creates a justifiable sense of urgency for theenergy consumer to resolve these issues. The various embodimentsdescribed in this disclosure should allow services-based organizationsto more readily identify opportunities and be retained for designing andinstalling the most economical solution. By leveraging products toidentify opportunities for improving voltage event mitigation andreduced recovery time, for example, energy consumers may improve theiroperational availability and increase their profitability.

The embodiments described in this disclosure may also create manyopportunities for cloud-based services. While the prospect of usingon-site software to evaluate, quantify, and mitigate voltage events maybe more ideal in some embodiments, direct (or substantially direct)participation/interaction with energy consumers may tend to promote manymore services and products sales opportunities. By evaluating thevoltage event data in the cloud, active engagement in a timelier mannerwith relevant information and practical solutions may yield furtherpossibilities.

As illustrated above, voltage sags/dips have a significant impact onindustrial equipment, processes, products, and ultimately a customer'sbottom-line. In embodiments, voltage sags/dips are the biggest (or closeto the biggest) source of power quality issues, and can originate bothinside and outside an energy consumer's facility. Using dynamic voltagetolerance curves and the other embodiments described herein will providethe ability to localize, quantify, and rectify the impact of voltagesags/dips and shorten event recovery time. Moreover, dynamic voltagetolerance curves provide the ability to target, design and validatecustom mitigative solutions and services, which helps the energyconsumer reduce interruptions to their operations, maximize their systemperformance and availability, increase their equipment life, and reducetheir total operating costs. In short, the embodiments disclosed in thisapplication may be incorporated in meters, gateways, on-site softwaresuch as PME, and cloud-based offers such as Power Advisor by SchneiderElectric.

As described above and as will be appreciated by those of ordinary skillin the art, embodiments of the disclosure herein may be configured as asystem, method, or combination thereof. Accordingly, embodiments of thepresent disclosure may be comprised of various means including hardware,software, firmware or any combination thereof.

It is to be appreciated that the concepts, systems, circuits andtechniques sought to be protected herein are not limited to use in theexample applications described herein (e.g., power monitoring systemapplications) but rather, may be useful in substantially any applicationwhere it is desired to manage power quality events in an electricalsystem. While particular embodiments and applications of the presentdisclosure have been illustrated and described, it is to be understoodthat embodiments of the disclosure not limited to the preciseconstruction and compositions disclosed herein and that variousmodifications, changes, and variations can be apparent from theforegoing descriptions without departing from the spirit and scope ofthe disclosure as defined in the appended claims.

Having described preferred embodiments, which serve to illustratevarious concepts, structures and techniques that are the subject of thispatent, it will now become apparent to those of ordinary skill in theart that other embodiments incorporating these concepts, structures andtechniques may be used. Additionally, elements of different embodimentsdescribed herein may be combined to form other embodiments notspecifically set forth above.

Accordingly, it is submitted that that scope of the patent should not belimited to the described embodiments but rather should be limited onlyby the spirit and scope of the following claims.

What is claimed is:
 1. A method for managing voltage event alarms in anelectrical system, the method comprising: processing electricalmeasurement data from, or derived from, energy-related signals capturedby at least one intelligent electronic device (IED) of a plurality ofIEDs to identify an anomalous voltage condition at a point ofinstallation of a respective one of the plurality of IEDs in theelectrical system, the anomalous voltage condition corresponding to ameasured IED voltage being above or greater than one or more upper alarmthresholds or below or less than one or more lower alarm thresholds;determining if the electrical system is affected by the identifiedanomalous voltage condition based, at least in part, on an evaluation ofat least one of a magnitude and a duration of the identified anomalousvoltage condition; and in response to determining that the electricalsystem is affected by the identified anomalous voltage condition,choosing at least one of a plurality of criteria to adjust at least oneof the upper alarm thresholds and/or at least one of the lower alarmthresholds.
 2. The method of claim 1, wherein the at least one of themagnitude and the duration of the identified anomalous voltage conditionused in the evaluation corresponds to a measured magnitude and ameasured duration of the identified anomalous voltage condition, and atleast one of the measured magnitude and the measured duration of theidentified anomalous voltage condition is compared to at least one of areference magnitude and a reference duration to determine if theelectrical system is affected by the identified anomalous voltagecondition.
 3. The method of claim 2, wherein the reference magnitude andthe reference duration are used to determine impact of the identifiedanomalous voltage condition on the electrical system.
 4. The method ofclaim 3, further comprising: in response to determining that the impactof identified anomalous voltage condition exceeds an impact threshold,determining that the electrical system is affected by the identifiedanomalous voltage condition.
 5. The method of claim 1, wherein thechosen at least one of the plurality of criteria includes recovery time.6. The method of claim 5, wherein the recovery time is associated withimpact of the anomalous voltage condition on the electrical system. 7.The method of claim 1, wherein the chosen at least one of the pluralityof criteria includes time(s) of occurrence of the anomalous voltagecondition.
 8. The method of claim 7, wherein a voltage event alarm oralarms associated with the one or more upper alarm thresholds and/or theone or more lower alarm thresholds is or are configured to be turned on,off, or adjusted in response to the time(s) of occurrence of theanomalous voltage condition meeting certain criteria.
 9. The method ofclaim 7, wherein the certain criteria includes a time or times at whichthe voltage event alarm or alarms are configured to be silenced ormuted.
 10. The method of claim 1, wherein the chosen at least one of theplurality of criteria includes the response of one or more load types tothe anomalous voltage condition.
 11. The method of claim 1, wherein thechosen at least one of the plurality of criteria includes input/output(I/O) data.
 12. The method of claim 1, wherein the chosen at least oneof the plurality of criteria includes a combination of criteria from theplurality of criteria.
 13. The method of claim 1, wherein the chosen atleast one of the plurality of criteria includes user configuredcriteria.
 14. The method of claim 1, wherein the chosen at least one ofthe plurality of criteria includes or is based on historical data. 15.The method of claim 14, wherein the historical data includes at leastone of non-impactful event data and impactful event data from historicalevents.
 16. The method of claim 15, wherein the historical events areassociated with one or more aspects, portions, zones or processes of theelectrical system.
 17. The method of claim 15, wherein the historicalevents are grouped and/or archived based on an end-user segment underconsideration.
 18. The method of claim 17, wherein the end-user segmentunder consideration includes at least one of: retail, offices, hotels,hospitals, data centers, food and beverages, oil and gas, industrial,automotive, utility, manufacturing, educational, governmental,residential and commercial.
 19. The method of claim 1, wherein theamount(s) and/or level(s) by which the at least one of the upper alarmthresholds and/or the at least one of the lower alarm thresholds is/areadjusted is/are based on an evaluation of at least one characteristicassociated with the identified anomalous voltage condition with respectto at least one of the plurality of criteria.
 20. The method of claim19, wherein the at least one characteristic associated with theidentified anomalous voltage condition includes at least one of themagnitude and the duration of the identified anomalous voltagecondition.
 21. The method of claim 1, wherein the upper alarm thresholdsand the lower alarm thresholds are indicated in at least one dynamictolerance curve associated with one or more loads monitored by therespective one of the plurality of IEDs in the electrical system,wherein the at least one dynamic tolerance curve characterizes responseof the one or more loads to power quality events including theidentified anomalous voltage condition, and an impact of the powerquality events on the electrical system, based on and/or responsive tothe at least one of a plurality of criteria.
 22. The method of claim 21,wherein the at least one dynamic tolerance curve includes a magnitudethreshold above the nominal voltage associated with a duration and amagnitude threshold below the nominal voltage associated with aduration, the magnitude threshold above the nominal voltage and themagnitude threshold below the nominal voltage based on and/or responsiveto the at least one of a plurality of criteria.
 23. The method of claim21, wherein the at least one dynamic tolerance curve includes aplurality of dynamic tolerance curves, the plurality of dynamictolerance curves including at least a first dynamic tolerance curve fora first day or time period and a second dynamic tolerance curve for asecond day or time period.
 24. A system for managing voltage eventalarms in an electrical system, the system comprising: at least oneprocessor; at least one memory device coupled to the at least oneprocessor, the at least one processor and the at least one memory deviceconfigured to: process electrical measurement data from, or derivedfrom, energy-related signals captured by at least one intelligentelectronic device (IED) of a plurality of IEDs to identify an anomalousvoltage condition at a point of installation of a respective one of theplurality of IEDs in the electrical system, the anomalous voltagecondition corresponding to a measured IED voltage being above or greaterthan one or more upper alarm thresholds or below or less than one ormore lower alarm thresholds; determine if the electrical system isaffected by the identified anomalous voltage condition based, at leastin part, on an evaluation of at least one of a magnitude and a durationof the identified anomalous voltage condition; and in response todetermining that the electrical system is affected by the identifiedanomalous voltage condition, choose at least one of a plurality ofcriteria to adjust at least one of the upper alarm thresholds and/or atleast one of the lower alarm thresholds.
 25. The system of claim 1,wherein the chosen at least one of the plurality of criteria includesuser configured criteria.
 26. The system of claim 1, wherein the chosenat least one of the plurality of criteria includes or is based onhistorical data.
 27. The system of claim 26, wherein the historical dataincludes at least one of non-impactful event data and impactful eventdata from historical events.
 28. The system of claim 27, wherein thehistorical events are associated with one or more aspects, portions,zones, processes of the electrical system.